diff --git a/.gitignore b/.gitignore index 03e33a4..339cc4c 100644 --- a/.gitignore +++ b/.gitignore @@ -167,20 +167,29 @@ models/*_meta.json models/*_vecnorm.pkl # Pre-trained hybrid_cv01 models — explicitly tracked +# VecNormalize stats are tracked too: play_ppo.py normalises observations from +# _vecnorm.pkl at inference, so a model is not reproducible without it. !models/ppo_hcv01_*.zip !models/ppo_hcv01_*_vecnorm.pkl !models/ppo_hcv01_*_meta.json !models/hybrid_cv01_s42.zip !models/hybrid_cv01_s42_meta.json +!models/hybrid_cv01_s42_vecnorm.pkl !models/hybrid_cv01_disc_s42.zip !models/hybrid_cv01_disc_s42_meta.json +!models/hybrid_cv01_disc_s42_vecnorm.pkl !models/hybrid_cv01_s5775.zip !models/hybrid_cv01_s5775_meta.json +!models/hybrid_cv01_s5775_vecnorm.pkl !models/hybrid_cv01_disc_s5775.zip !models/hybrid_cv01_disc_s5775_meta.json +!models/hybrid_cv01_disc_s5775_vecnorm.pkl # Q-agent test artifacts tests/test_working_directory/*.json +# Claude Code local session/config +.claude/ + # Local dev docs (not for the repo) for_sig.md diff --git a/docs/source/_static/episode_cont_s42_v5p0.png b/docs/source/_static/episode_cont_s42_v5p0.png index a5edaa1..8ab6d34 100644 Binary files a/docs/source/_static/episode_cont_s42_v5p0.png and b/docs/source/_static/episode_cont_s42_v5p0.png differ diff --git a/docs/source/_static/episode_cont_s42_v9p0.png b/docs/source/_static/episode_cont_s42_v9p0.png index af07225..1d063e2 100644 Binary files a/docs/source/_static/episode_cont_s42_v9p0.png and b/docs/source/_static/episode_cont_s42_v9p0.png differ diff --git a/docs/source/_static/episode_disc_s42_v1p0.png b/docs/source/_static/episode_disc_s42_v1p0.png deleted file mode 100644 index f6f510c..0000000 Binary files a/docs/source/_static/episode_disc_s42_v1p0.png and /dev/null differ diff --git a/docs/source/_static/episode_disc_s42_v1p2.png b/docs/source/_static/episode_disc_s42_v1p2.png new file mode 100644 index 0000000..641ec83 Binary files /dev/null and b/docs/source/_static/episode_disc_s42_v1p2.png differ diff --git a/docs/source/_static/episode_disc_s42_v5p0.png b/docs/source/_static/episode_disc_s42_v5p0.png index ba87873..8b0897f 100644 Binary files a/docs/source/_static/episode_disc_s42_v5p0.png and b/docs/source/_static/episode_disc_s42_v5p0.png differ diff --git a/docs/source/_static/episode_disc_s42_v9p0.png b/docs/source/_static/episode_disc_s42_v9p0.png index e30c0df..6176064 100644 Binary files a/docs/source/_static/episode_disc_s42_v9p0.png and b/docs/source/_static/episode_disc_s42_v9p0.png differ diff --git a/docs/source/_static/fig1_training_s5775.png b/docs/source/_static/fig1_training_s5775.png index f4beff7..7c5436a 100644 Binary files a/docs/source/_static/fig1_training_s5775.png and b/docs/source/_static/fig1_training_s5775.png differ diff --git a/docs/source/_static/fig2_sweep_s5775.png b/docs/source/_static/fig2_sweep_s5775.png index 9128d3b..015900d 100644 Binary files a/docs/source/_static/fig2_sweep_s5775.png and b/docs/source/_static/fig2_sweep_s5775.png differ diff --git a/docs/source/_static/fig_sweep_s42.png b/docs/source/_static/fig_sweep_s42.png index 3f41a72..ec166a5 100644 Binary files a/docs/source/_static/fig_sweep_s42.png and b/docs/source/_static/fig_sweep_s42.png differ diff --git a/docs/source/_static/fig_training_s42.png b/docs/source/_static/fig_training_s42.png index 96f9a24..ec58b68 100644 Binary files a/docs/source/_static/fig_training_s42.png and b/docs/source/_static/fig_training_s42.png differ diff --git a/docs/source/_static/hybrid_cv01_disc_s42_detail.png b/docs/source/_static/hybrid_cv01_disc_s42_detail.png index 9e47095..34d2ecd 100644 Binary files a/docs/source/_static/hybrid_cv01_disc_s42_detail.png and b/docs/source/_static/hybrid_cv01_disc_s42_detail.png differ diff --git a/docs/source/_static/hybrid_cv01_disc_s5775_detail.png b/docs/source/_static/hybrid_cv01_disc_s5775_detail.png index 8f255a9..5a03b86 100644 Binary files a/docs/source/_static/hybrid_cv01_disc_s5775_detail.png and b/docs/source/_static/hybrid_cv01_disc_s5775_detail.png differ diff --git a/docs/source/_static/hybrid_cv01_s42_detail.png b/docs/source/_static/hybrid_cv01_s42_detail.png index 1d4b896..025b348 100644 Binary files a/docs/source/_static/hybrid_cv01_s42_detail.png and b/docs/source/_static/hybrid_cv01_s42_detail.png differ diff --git a/docs/source/_static/hybrid_cv01_s5775_detail.png b/docs/source/_static/hybrid_cv01_s5775_detail.png index e517623..6c629df 100644 Binary files a/docs/source/_static/hybrid_cv01_s5775_detail.png and b/docs/source/_static/hybrid_cv01_s5775_detail.png differ diff --git a/docs/source/reward_comparison.md b/docs/source/reward_comparison.md index 2516d95..1ed174d 100644 --- a/docs/source/reward_comparison.md +++ b/docs/source/reward_comparison.md @@ -4,7 +4,7 @@ The `hybrid_cv01` reward was trained on the anti-pendulum crane environment with identical PPO hyperparameters across two seeds (5775 and 42) and two action spaces (continuous and -discrete). This document covers training dynamics, seed sensitivity, and the effect of +discrete). This document covers training dynamics, seed sensitivity, and the effect of discretising the action space on final position precision. --- @@ -13,14 +13,14 @@ discretising the action space on final position precision. The agent receives a dense signal at every step composed of several explicit penalty terms: position offset from centre, crane velocity, crane acceleration, and total mechanical energy. -Each term has a hand-tuned weight. The agent is told *what to achieve* (stop here, stop +Each term has a hand-tuned weight. The agent is told *what to achieve* (stop here, stop gently, use little energy) directly. $$r_t = \underbrace{-\!\left(g\,z_\mathrm{load} + \tfrac{1}{2}v_\mathrm{load}^2\right)}_{\text{pendulum energy}} - 0.1\,|x| - 0.1\,\dot{x}^2 + \delta_\mathrm{crash}\cdot(-5)$$ The *pendulum energy* term is the negative total mechanical energy of the load: most positive -(least negative) when the pendulum hangs stationary, decreasing as it swings. The $-|x|$ -and $-\dot{x}^2$ terms penalise crane offset and crane speed directly. The $-5$ terminal +(least negative) when the pendulum hangs stationary, decreasing as it swings. The $-|x|$ +and $-\dot{x}^2$ terms penalise crane offset and crane speed directly. The $-5$ terminal penalty fires once on rail collision. *Notation: $x$ = crane position (m), $\dot{x}$ = crane velocity (m/s), @@ -62,18 +62,23 @@ Settle step and simulated seconds are numerically equal.* | clip\_range | 0.2 | | ent\_coef | 0.0 | +For the continuous action space, the Gaussian policy's initial standard deviation +(`log_std_init`) is auto-derived from $a_\text{max}$ rather than left at Stable-Baselines3's +default — see §3.1's `entropy_loss` note and the project's `ppo_agent.py` docstring for why +this matters for continuous control at this action scale. + --- ## 3 Metric glossary ### 3.1 PPO algorithm metrics -These quantities are produced by the PPO optimiser at each update step. They describe how +These quantities are produced by the PPO optimiser at each update step. They describe how the *learning process itself* is behaving, not the crane directly. `explained_variance` : The value function (critic network) continuously predicts the total future reward from the - current state. Explained variance measures how accurate those predictions are. + current state. Explained variance measures how accurate those predictions are. Think of it like the innovation in a Kalman filter: a value of **1.0** means perfect predictions; **0.0** means the critic is no better than always guessing the average; **negative** means the critic is actively worse than guessing — it is confident but wrong. @@ -81,76 +86,76 @@ the *learning process itself* is behaving, not the crane directly. `value_loss` : The mean-squared error between the critic's predictions and the actual observed returns. - Lower is better. Healthy behaviour: starts high, falls monotonically, converges near zero. + Lower is better. Healthy behaviour: starts high, falls monotonically, converges near zero. *Trap*: a very low value_loss coexisting with negative explained_variance signals that the critic has collapsed to predicting a near-constant for all states (zero variance in predictions → low MSE, but zero predictive power). `approx_kl` : KL divergence between the old policy and the updated policy after one optimisation step. - Measures how much the agent's decision-making changed this update. Healthy: small and - stable (0.01 – 0.05). Large values indicate an aggressive or destabilising update. + Measures how much the agent's decision-making changed this update. Healthy: small and + stable (0.01 – 0.05). Large values indicate an aggressive or destabilising update. `clip_fraction` -: PPO limits how large each policy update can be via a clipping ratio. This metric is the +: PPO limits how large each policy update can be via a clipping ratio. This metric is the fraction of training samples that hit that limiter — analogous to a saturation nonlinearity - in a controller. Healthy: 0.10 – 0.25. Consistently above 0.35 suggests the optimiser + in a controller. Healthy: 0.10 – 0.25. Consistently above 0.35 suggests the optimiser is fighting against the clip and update quality may degrade. `entropy_loss` -: Stable-Baselines3 logs $-\text{entropy}$ of the policy distribution. More negative = - more random / exploratory. The value rises toward zero and beyond as the policy becomes - deterministic. With `ent_coef = 0` (as in both configs here), there is no explicit - entropy bonus, so the policy is free to collapse to fully deterministic — expected - behaviour in later training. +: Stable-Baselines3 logs $-\text{entropy}$ of the policy distribution. More negative = + more random / exploratory. With `ent_coef = 0` (as in both configs here), there is no + explicit entropy bonus, so nothing in the loss opposes entropy drifting up over training — + a rising `entropy_loss` here does not by itself indicate a problem; what matters is + whether the resulting action distribution stays well inside the action box (see §3.3). `policy_gradient_loss` -: The PPO surrogate objective value. Negative means the gradient is pointing in a - direction that increases expected reward. Should remain stable and negative. +: The PPO surrogate objective value. Negative means the gradient is pointing in a + direction that increases expected reward. Should remain stable and negative. ### 3.2 Training performance metrics `ep_len_mean` -: Average episode length (simulation steps). The episode ends when the crane hits the rail - (crash) or reaches `max_episode_steps` (1 000 steps). Healthy: grows from ~20 (constant +: Average episode length (simulation steps). The episode ends when the crane hits the rail + (crash) or reaches `max_episode_steps` (1 000 steps). Healthy: grows from ~20 (constant crashing) to 1 000. `rew_per_step` : Total episode reward divided by episode length — a normalised reward signal that removes - the confounding effect of episode length changing over training. Healthy: increases + the confounding effect of episode length changing over training. Healthy: increases (becomes less negative) over time. `rail_hit_pct` -: Percentage of episodes that ended by the crane hitting the rail. Healthy: starts at 100% +: Percentage of episodes that ended by the crane hitting the rail. Healthy: starts at 100% (random policy crashes immediately), falls to 0% and stays there. ### 3.3 Physical crane metrics -These appear in the log only once some episodes survive without crashing. They measure what +These appear in the log only once some episodes survive without crashing. They measure what the crane actually does at episode end, independent of the reward formulation. `mean_x_pos_abs` -: Mean absolute crane position from centre (metres). Falls toward 0 as training progresses. +: Mean absolute crane position from centre (metres). Falls toward 0 as training progresses. `mean_x_vel_abs` -: Mean absolute crane velocity (m/s). Falls toward 0 as the crane learns to stop cleanly. +: Mean absolute crane velocity (m/s). Falls toward 0 as the crane learns to stop cleanly. `mean_energy` -: Residual mechanical energy in the crane–pendulum system at episode end. Falls toward 0 +: Residual mechanical energy in the crane–pendulum system at episode end. Falls toward 0 as motion ceases. `mean_theta_dot_abs` -: Mean absolute pendulum angular velocity (rad/s). Falls toward 0 as the pendulum damps +: Mean absolute pendulum angular velocity (rad/s). Falls toward 0 as the pendulum damps out. `mean_theta_dev` -: Mean angular deviation of the pendulum from the upright position. Falls toward 0 as the +: Mean angular deviation of the pendulum from the upright position. Falls toward 0 as the pendulum stays balanced. `t_min_settle_step` : The simulation step at which the episode's $t_\text{min}$ value first drops below the settle threshold and stays there — i.e. the moment the crane is judged to be - *effectively at rest*. The simulation timestep is **dt = 1.0 s**, so settle step and + *effectively at rest*. The simulation timestep is **dt = 1.0 s**, so settle step and simulated seconds are numerically equal (settle\_step 87 = 87 s of simulation time). Lower is faster convergence. @@ -161,58 +166,54 @@ the crane actually does at episode end, independent of the reward formulation. ![Training curves for hybrid_cv01_s5775 — continuous and discrete](_static/fig1_training_s5775.png) *Figure 1. Twelve training metrics over 3 M steps for seed 5775 — continuous (blue, solid) -and discrete (orange, dashed). Key panels: rail\_hit%↓ (top-left), expl\_var↑ (row 2 col 1), +and discrete (orange, dashed). Key panels: rail\_hit%↓ (top-left), expl\_var↑ (row 2 col 1), value\_loss↓ (row 2 col 2), |x|↓ (bottom row).* ### 4.1 Summary | Metric | cont | disc | |---|---|---| -| rail\_hit → 0% (permanent) | 850 k steps | **850 k steps** | -| ep\_len hits maximum (1 000) | 1.2 M steps | **850 k steps** | -| Value instabilities | 1 minor (1.35 M: EV → 0.019) | **none** | -| Explained variance at 3 M | 0.925 | **0.954** | -| Mean \|x\| at 3 M (training log) | 0.027 m | **≈ 0 m** | +| rail\_hit → 0% (permanent) | 950 k steps | **800 k steps** | +| ep\_len hits maximum (1 000) | 1.0 M steps | **900 k steps** | +| Value instabilities | none | **none** | +| Explained variance at 3 M | **0.986** | 0.971 | +| Mean \|x\| at 3 M (training log) | 0.015 m | **≈ 0 m** | -Both variants eliminate crashes at 850 k steps. The discrete variant never produces an instability -event. Its final EV (0.954) is the highest of the two s5775 variants. +Both variants eliminate crashes early and permanently — discrete slightly ahead (800 k vs +950 k steps). Neither variant produces a value-function instability event. Explained +variance is close between the two, with continuous now marginally ahead (0.986 vs 0.971). ### 4.2 Phase 1 — Survival (0 – 700 k steps) -The agent starts with a random policy. The crane hits the rail within roughly 20 steps -every episode. Episode length grows slowly as the agent learns to delay the inevitable -crash. Physical metrics are not meaningful here because all episodes end in crashes. +The agent starts with a random policy. The crane hits the rail within roughly 20 steps +every episode. Episode length grows slowly as the agent learns to delay the inevitable +crash. Physical metrics are not meaningful here because all episodes end in crashes. ### 4.3 Phase 2 — Crash elimination `hybrid_cv01` eliminates crashes decisively: rail_hit_pct falls from 100% to 0% by -**850 k steps** (continuous) / **850 k steps** (discrete) and never returns. The explicit +**950 k steps** (continuous) / **800 k steps** (discrete) and never returns. The explicit position-penalty terms give the agent a direct incentive to stay away from the rail at every step. -### 4.4 Phase 3 — Value function event (continuous only) - -When episode length first reaches `max_episode_steps` (1 000), the statistics of the reward -signal seen by the critic change suddenly: instead of a mix of short crash-terminated -episodes and longer ones, all episodes are now exactly the same length with small, steady -negative rewards. The critic's previously calibrated predictions become systematically wrong. +### 4.4 Phase 3 — Mid-training convergence -This triggers a sharp downward spike in `expl_var`: explained_variance goes negative -(the critic is now *confidently wrong*) and value_loss paradoxically drops (the critic has -collapsed to predicting a near-constant value for all states — low MSE, but zero useful -information content). The single event at 1.35 M (EV → 0.019) recovers within ~130 k -steps and the value function then climbs monotonically to **EV = 0.928** by 3 M. -The discrete variant produces no such event. +Both variants continue to improve smoothly through the middle of training, with explained +variance climbing and value loss falling in both cases. No instability events (sharp EV +collapses or reward-distribution discontinuities) are observed in either variant for this +training run. ### 4.5 Phase 4 — Convergence (1.5 M → 3 M) -Explained variance climbs monotonically in both variants. Policy becomes increasingly -deterministic (entropy_loss → 0). The discrete variant converges to EV = 0.954 vs -EV = 0.925 for continuous — a more precisely calibrated critic with less residual -uncertainty in the value estimates. +Explained variance climbs monotonically in both variants. Policy becomes increasingly +deterministic in the discrete case (entropy_loss → 0); the continuous policy's entropy +continues to rise slowly through 3 M steps without destabilising training. The continuous +variant reaches marginally higher final EV (0.986) than discrete (0.971) — both represent +a well-calibrated critic, with the continuous variant's larger action space no longer +imposing a value-estimation penalty. -**Why does EV matter?** The actor (policy) is updated using gradients derived partly from -the critic's value estimates. Higher EV means each policy update is based on a less biased +**Why does EV matter?** The actor (policy) is updated using gradients derived partly from +the critic's value estimates. Higher EV means each policy update is based on a less biased signal, producing tighter convergence. --- @@ -220,76 +221,89 @@ signal, producing tighter convergence. ## 5 Speed sweep — seed 5775 `hybrid_cv01_s5775` was evaluated at 100 initial crane speeds uniformly spaced from −10.0 -to +10.0 m/s (step 0.2 m/s) with the pendulum initially at rest upright. No randomised -start. Each episode runs for the full 1 000-step budget. +to +10.0 m/s (step 0.2 m/s) with the pendulum initially at rest upright. No randomised +start. Each episode runs for the full 1 000-step budget. ![Speed sweep comparison — hybrid_cv01_s5775, continuous vs discrete](_static/fig2_sweep_s5775.png) -*Figure 2. Speed sweep across ±10 m/s — seed 5775, continuous (blue, solid) vs discrete -(orange, dashed). Top row: final crane position (cm), final crane velocity (m/s), settle -step vs |speed|. Bottom row: final pendulum angle (rad), final pendulum angular velocity -(rad/s), final crane acceleration (m/s²).* +*Figure 2. Speed sweep across ±10 m/s — seed 5775, continuous (blue) vs discrete (orange). +Top row: final crane position (cm), final crane velocity (m/s), settle step vs |speed|. +Bottom row: final pendulum angle (rad), final pendulum angular velocity (rad/s), final +crane acceleration (m/s²). The single discrete spike near +8.2 m/s (visible in every panel) +is the lone rail-contact episode discussed in §5.2.* ### 5.1 Summary | Metric | cont | disc | |---|---|---| -| Crash-free episodes | 100/100 | 100/100 | +| Crash-free episodes | 100/100 | 99/100 | | Non-converging | 0 | **0** | -| Mean \|x\_pos\| | 0.695 cm | **≈ 0 cm** (machine ε) | -| Settle-step range | 34–167 steps | **7–126 steps** | -| Mean settle step | 118.6 | **71.8** | +| Mean \|x\_pos\| | 0.66 cm | **≈ 0 cm** (machine ε, 99/100) | +| Settle-step range | 67–246 steps | **7–129 steps** | +| Mean settle step | 184.5 | **73.7** | ### 5.2 Robustness across the speed range -Both variants are **100% crash-free** across the full ±10 m/s range. This is a consequence -of `randomize_start = true` during training: the agent was exposed to the full range of -initial conditions during learning, and the learned policy generalises cleanly. +The continuous variant is **100% crash-free** across the full ±10 m/s range. The discrete +variant crashes once, at a single swept speed (8.2 m/s, rail contact at step 16) — the +immediately adjacent speeds (8.0 and 8.4 m/s) settle normally, so this is an isolated +single-run transient (one seed, one episode per speed), not a systematic failure. Both +variants' broad robustness is a consequence of `randomize_start = true` during training: +the agent was exposed to a range of initial conditions during learning, and the learned +policy generalises far beyond that range. -Final crane position and $t_\text{min}$ are **essentially constant across all 100 speed -points** (flat curves in Figure 2, left and centre panels) — the agent always converges to -the same physical attractor regardless of how fast the crane was initially moving. +Final crane position is **essentially constant across nearly all 100 speed points** (flat +curve in Figure 2, left panel) — the agent always converges to the same physical attractor +regardless of how fast the crane was initially moving. Settle step is **not** constant: it +rises smoothly with |initial speed| (Figure 2, centre panel) — more initial momentum takes +more time to brake, with no anomalous outliers in either variant. ### 5.3 Effect of discretisation on precision With a continuous action space the agent can select any force in $[-a_\text{max}, +a_\text{max}]$, including sub-optimal intermediate values that balance the position penalty -against other terms. With `Discrete(3)` there is no intermediate force: every step is -either full brake, coast, or full drive. The agent must commit to a bang-bang sequence +against other terms. With `Discrete(3)` there is no intermediate force: every step is +either full brake, coast, or full drive. The agent must commit to a bang-bang sequence that lands on $x = 0$ exactly, which the dense position-penalty term directly rewards. -The result is machine-epsilon final position (1e-14 – 1e-16 m) for all 100 evaluation -speeds vs 0.64 cm for the continuous variant. +The result is machine-epsilon final position (1e-14 – 1e-17 m) for 99 of 100 evaluation +speeds (seed 5775; the hundredth is the single rail-contact episode from §5.2) vs a +constant 0.66 cm for the continuous variant. Seed 42's discrete run is slightly less +clean — see §6.2. ### 5.4 Settle step and speed Settle step rises roughly linearly with |initial speed|: higher initial momentum requires -more braking time. With dt = 1.0 s per step, the range of 6–87 steps (discrete) and -28–143 steps (continuous) corresponds to **6–87 s** and **28–143 s** of simulated time -respectively. At speed = 10 m/s the theoretical bang-bang minimum is $v/a = 10/0.1 = 100$ s; -the discrete agent operates at ~87 s — very close to the physical optimum. +more braking time. With dt = 1.0 s per step, the range of 67–246 steps (continuous) and +7–129 steps (discrete) corresponds to **67–246 s** and **7–129 s** of simulated time +respectively. At speed = ±10 m/s the discrete agent settles at ~112–117 steps — close to +the theoretical bang-bang minimum $v/a = 10/0.1 = 100$ s; the continuous agent takes +considerably longer (~243 s) at the same speed, reflecting its smoother, less aggressive +braking profile. ### 5.5 Detailed sweep metrics ![hybrid_cv01_s5775 continuous — detailed sweep](_static/hybrid_cv01_s5775_detail.png) *Figure 3. `hybrid_cv01_s5775` continuous — nine sweep metrics across ±10 m/s. -Top row: crash rate, reward per step, energy fraction. Middle row: settle step, final -crane position, final crane velocity. Bottom row: final crane acceleration, pendulum +Top row: crash rate, reward per step, energy fraction. Middle row: settle step, final +crane position, final crane velocity. Bottom row: final crane acceleration, pendulum angle, pendulum angular velocity.* ![hybrid_cv01_disc_s5775 — detailed sweep](_static/hybrid_cv01_disc_s5775_detail.png) *Figure 4. `hybrid_cv01_disc_s5775` (discrete) — nine sweep metrics across ±10 m/s. -Note the x\_pos\_m panel: all values at machine-epsilon level (1e-14 – 1e-16 m).* +x\_pos\_m panel: machine-epsilon level (1e-14 – 1e-17 m) for 99 of 100 speeds; one +rail-contact episode at 8.2 m/s (visible as the single nocrash%↑ dip).* --- ## 6 Seed 42 Seed 42 covers both continuous and discrete action spaces, following the same structure as -§4–§5 for seed 5775. The key question: does the discrete advantage (machine-epsilon -precision, no non-converging episodes) hold under a different random seed? +§4–§5 for seed 5775. The key question: does the discrete advantage (near-machine-epsilon +precision, no non-converging episodes) hold under a different random seed? It largely does, +with a single-run caveat — see §6.2. ### 6.1 Training dynamics @@ -300,95 +314,115 @@ and discrete (orange, dashed).* | Metric | cont | disc | |---|---|---| -| rail\_hit → 0% (permanent) | **900 k steps** | 1 000 k steps | -| ep\_len hits maximum (1 000) | 950 k steps | 1 200 k steps | +| rail\_hit → 0% (permanent) | **700 k steps** | 1 050 k steps | +| ep\_len hits maximum (1 000) | **750 k steps** | 1 150 k steps | | Value instabilities | none | none | -| Explained variance at 3 M | 0.886 | **0.931** | -| Mean \|x\| at 3 M (training log) | 0.032 m | **≈ 0 m** | +| Explained variance at 3 M | **0.987** | 0.928 | +| Mean \|x\| at 3 M (training log) | 0.008 m | **≈ 0 m** | + +For seed 42 the continuous variant eliminates crashes substantially earlier than discrete +(700 k vs 1 050 k steps) and reaches a higher final EV (0.987 vs 0.928) — the same EV +pattern as seed 5775: continuous now edges out discrete on critic accuracy for both seeds. -For seed 42 the continuous variant eliminates crashes earlier (900 k vs 1 000 k), in -contrast to seed 5775 where discrete was faster. Both reach full crash-elimination and the -discrete variant achieves higher final EV by 3 M. +The discrete `expl_var` curve in Figure 5 dips sharply (to ≈ 0.04) around 800 k–1.05 M +steps before recovering to 0.93. This coincides with the discrete crash-elimination window +(`rail_hit%` still falling over the same interval): the policy is changing fast enough that +the critic briefly loses its fit, then re-converges once the policy stabilises. It is a +normal pre-convergence transient, not a value-function instability event — EV is monotone +non-decreasing from 1.05 M onward for both variants. ### 6.2 Speed sweep ![Speed sweep — hybrid_cv01_s42, continuous vs discrete](_static/fig_sweep_s42.png) -*Figure 6. Speed sweep across ±10 m/s — seed 42, continuous (blue, solid) vs discrete -(orange, dashed). Top row: final crane position (cm), final crane velocity (m/s), settle -step vs |speed|. Bottom row: final pendulum angle (rad), final pendulum angular velocity -(rad/s), final crane acceleration (m/s²).* +*Figure 6. Speed sweep across ±10 m/s — seed 42, continuous (blue) vs discrete (orange). +Top row: final crane position (cm), final crane velocity (m/s), settle step vs |speed|. +Bottom row: final pendulum angle (rad), final pendulum angular velocity (rad/s), final +crane acceleration (m/s²). The ten discrete downspikes to −10 cm in the |x| panel are the +±0.1 m offset attractor discussed below.* | Metric | cont | disc | |---|---|---| | Crash-free episodes | 100/100 | 100/100 | | Non-converging | 0 | **0** | -| Mean \|x\_pos\| | 0.55 cm | **≈ 0 cm** (machine ε) | -| Settle-step range | 25–835 steps† | **6–129 steps** | -| Mean settle step | 122.1 | **74.0** | - -Both variants achieve zero non-converging episodes. The discrete variant continues to achieve -machine-epsilon position for all 100 speeds. Three outlier settle steps appear in the -continuous policy (0.8, 1.2, and 3.4 m/s), with speed 1.2 producing an anomalously late -settle (step 835) and elevated final position (4.9 cm); the remaining 97 episodes converge -within 25–216 steps. +| Mean \|x\_pos\| | 0.39 cm | ≈ 1.0 cm (90/100 at machine ε) | +| Settle-step range | 95–218 steps | **14–126 steps** | +| Mean settle step | 168.8 | **74.2** | + +Both variants achieve zero non-converging episodes and zero crashes across the full ±10 m/s +range. The discrete variant reaches machine-epsilon final position for **90 of 100** swept +speeds; the other ten (−8.4, −6.4, −5.0, −3.8, +0.8, +1.0, +3.4, +3.6, +5.2, +9.0 m/s) +converge instead to a **±0.1 m offset attractor** — the crane parks one deadband-width off +centre and toggles ±$a_\text{max}$ across it rather than landing exactly on $x = 0$. This +lifts the discrete mean |x\_pos| to ≈ 1.0 cm, still below the continuous variant's 0.39 cm +but no longer machine-epsilon everywhere. The effect is a single-run artefact: one training +seed, one episode per evaluated speed, no averaging — seed 5775's discrete run misses only +once (§5), and an earlier unseeded seed-42 discrete run reached $x = 0$ at every speed. +Continuous-variant final position (0.39 cm for s42, 0.66 cm for s5775) has no anomalous +outlier speeds in either seed. ### 6.3 Detailed sweep metrics ![hybrid_cv01_s42 continuous — detailed sweep](_static/hybrid_cv01_s42_detail.png) -*Figure 7. `hybrid_cv01_s42` continuous — nine sweep metrics across ±10 m/s. -Three anomalous settle-step outliers (0.8, 1.2, 3.4 m/s) are visible in the settle-step panel; speed 1.2 also shows elevated final position.* +*Figure 7. `hybrid_cv01_s42` continuous — nine sweep metrics across ±10 m/s. Settle step +scales smoothly with |speed| with no anomalous outliers; final position is flat across the +whole range.* ![hybrid_cv01_disc_s42 — detailed sweep](_static/hybrid_cv01_disc_s42_detail.png) *Figure 8. `hybrid_cv01_disc_s42` (discrete) — nine sweep metrics across ±10 m/s. -All 100 episodes converge; x\_pos at machine-epsilon level throughout.* +All 100 episodes converge crash-free; x\_pos at machine-epsilon level for 90 of 100 speeds, +with ten at a ±0.1 m offset attractor (see §6.2 — visible as ten downward spikes to +−0.1 m in the x\_pos\_m panel).* ### 6.4 Episode trajectories The plots below show time-series for individual episodes: crane position, velocity, pendulum angle, pendulum angular velocity, crane acceleration, and reward — one panel -per quantity, over the full episode. They complement the sweep figures by showing *how* +per quantity, over the full episode. They complement the sweep figures by showing *how* the agent moves, not just the final values. -**A — Discrete, 1.0 m/s — fast bang-bang settle** +**A — Discrete, 1.2 m/s — fast bang-bang settle** -![Episode: disc s42, start speed +1.0 m/s](_static/episode_disc_s42_v1p0.png) +![Episode: disc s42, start speed +1.2 m/s](_static/episode_disc_s42_v1p2.png) -*Figure 9. `hybrid_cv01_disc_s42`, start speed +1.0 m/s, 250 steps. -Settles at step 28. Three-phase bang-bang: full brake → coast → done.* +*Figure 9. `hybrid_cv01_disc_s42`, start speed +1.2 m/s. +Settles at step 23, final position machine-epsilon. Three-phase bang-bang: +full brake → coast → done.* **B — Continuous, 5.0 m/s — smooth mid-range convergence** ![Episode: cont s42, start speed +5.0 m/s](_static/episode_cont_s42_v5p0.png) -*Figure 10. `hybrid_cv01_s42` (continuous), start speed +5.0 m/s, 200 steps. -Settles at step 87, final position −1.4 cm. Action (acc panel) is smooth and -sub-maximal throughout — the agent blends intermediate forces.* +*Figure 10. `hybrid_cv01_s42` (continuous), start speed +5.0 m/s, 300 steps. +Settles at step 175, final position −0.39 cm. Action (acc panel) is a smoothly decaying +oscillation rather than persistent full-amplitude bang-bang switching.* **C — Discrete, 5.0 m/s — bang-bang contrast at same speed** ![Episode: disc s42, start speed +5.0 m/s](_static/episode_disc_s42_v5p0.png) -*Figure 11. `hybrid_cv01_disc_s42`, start speed +5.0 m/s, 200 steps. -Settles at step 50 (37 steps earlier than continuous), final position machine-epsilon. -Action is always ±0.1 or 0 — no intermediate values.* +*Figure 11. `hybrid_cv01_disc_s42`, start speed +5.0 m/s. +Settles at step 73 (102 steps earlier than the continuous variant at this speed), final +position machine-epsilon. Action is always ±0.1 or 0 — no intermediate values.* **D — Continuous, 9.0 m/s — convergence** ![Episode: cont s42, start speed +9.0 m/s](_static/episode_cont_s42_v9p0.png) -*Figure 12. `hybrid_cv01_s42` (continuous), start speed +9.0 m/s, 400 steps. -Settles at step 148, final position 0.51 cm. Previously non-converging in an earlier -training run; the retrained model handles +9.0 m/s cleanly.* +*Figure 12. `hybrid_cv01_s42` (continuous), start speed +9.0 m/s, 300 steps. +Settles at step 201, final position −0.39 cm. No crash and no non-convergence at this +speed, consistent with the model's zero-crash record across the full ±10 m/s sweep.* **E — Discrete, 9.0 m/s — bang-bang at high speed** ![Episode: disc s42, start speed +9.0 m/s](_static/episode_disc_s42_v9p0.png) -*Figure 13. `hybrid_cv01_disc_s42`, start speed +9.0 m/s, 200 steps. -Settles at step 76, final position machine-epsilon.* +*Figure 13. `hybrid_cv01_disc_s42`, start speed +9.0 m/s. +Settles at step 111 at a −0.10 m offset — one of the ten swept speeds that converge to the +±0.1 m attractor rather than exactly zero (§6.2), shown here for contrast with the +machine-epsilon cases above.* --- @@ -398,41 +432,53 @@ Settles at step 76, final position machine-epsilon.* | Metric | s5775 · cont | s5775 · disc | s42 · cont | s42 · disc | |---|---|---|---|---| -| Time to crash-free | 850 k steps | **800 k steps** | 900 k steps | 1 000 k steps | -| EV at 3 M | 0.925 | **0.954** | 0.886 | 0.931 | -| Final position | 0.695 cm | **≈ 0 cm** | 0.55 cm | **≈ 0 cm** | -| Settle range | 34–167 s | **7–126 s** | 25–835 s† | **6–129 s** | +| Time to crash-free | 950 k steps | **800 k steps** | **700 k steps** | 1 050 k steps | +| EV at 3 M | **0.986** | 0.971 | **0.987** | 0.928 | +| Final position | 0.66 cm | **≈ 0 cm** (99/100) | 0.39 cm | ≈ 1.0 cm (90/100 ≈ 0) | +| Settle range | 67–246 s | **7–129 s** | 95–218 s | **14–126 s** | +| Sweep crashes (of 100) | 0 | 1 | 0 | 0 | | Non-converging | 0 | **0** | 0 | **0** | -† three outlier speeds (0.8, 1.2, 3.4 m/s) account for the elevated ceiling. - ### 7.2 Training consistency -Both seeds eliminate crashes at ~850 k steps (continuous) — early and reproducibly. The -dense, multi-term reward provides unambiguous per-step feedback on position, velocity, and -energy simultaneously, giving the value function a well-constrained learning target. A -single minor value function event at 1.35 M steps (EV → 0.019, recovered within 130 k -steps) is the only instability observed, and only in the continuous variant. +Both seeds eliminate crashes within roughly the first third of training and never return +to crashing thereafter. The dense, multi-term reward provides unambiguous per-step feedback +on position, velocity, and energy simultaneously, giving the value function a +well-constrained learning target. No value-function instability events were observed in +either variant, for either seed. ### 7.3 Seed robustness -Performance degrades modestly across seeds (0.695 → 0.55 cm position, both 100% crash-free -for continuous). The multi-term reward constrains the value landscape tightly, leaving -little room for seed-specific failure modes. +Continuous-variant final position varies modestly across seeds (0.66 cm for s5775 vs +0.39 cm for s42) with no outlier speeds. The discrete variant is at machine-epsilon +precision for 99/100 (s5775) and 90/100 (s42) swept speeds; the misses are one +rail-contact transient (s5775, at 8.2 m/s) and a ±0.1 m offset attractor at ten seed-42 +speeds. Both are single-run artefacts — one seed, one episode per speed — not systematic: +the continuous side is crash-free at every speed for both seeds, the discrete side is +crash-free at every speed for seed 42 and at 99/100 for seed 5775, and an earlier unseeded +seed-42 discrete run reached $x = 0$ at every speed. The multi-term reward constrains the +value landscape tightly, leaving only these small single-run deviations. ### 7.4 Effect of discretisation -Switching to `Discrete(3)` (bang-bang actions) consistently improves final precision and -EV across both seeds, confirmed by four independent training runs. - -The precision gain is the most striking result: final crane position drops from 0.64–1.46 cm -(continuous) to machine-epsilon level (1e-14 – 1e-16 m, discrete) for every evaluated -speed in both seeds. With a continuous action space the agent can settle at sub-optimal -intermediate forces; with `Discrete(3)` it must commit to a bang-bang sequence that lands -precisely at $x = 0$, which the dense position-penalty term directly rewards. - -Mean settle step: 118.6 → 71.8 s (s5775) and 122.1 → 74.0 s (s42). -The converged settle ranges are 7–126 s (s5775) and 6–129 s (s42) for discrete. +Switching to `Discrete(3)` (bang-bang actions) improves final position precision for the +large majority of evaluated speeds — final crane position drops from a constant +0.39–0.66 cm (continuous) to machine-epsilon level (1e-14 – 1e-17 m) for 90/100 (seed 42) +and 99/100 (seed 5775) swept speeds. With a continuous action space the agent can settle +at sub-optimal intermediate forces; with `Discrete(3)` it must commit to a bang-bang +sequence that lands precisely at $x = 0$, which the dense position-penalty term directly +rewards. The discrete exceptions are a ±0.1 m offset attractor at ten seed-42 speeds and +one rail-contact transient at seed 5775; both are single-run (one seed, one episode per +speed), and an earlier unseeded seed-42 discrete run reached $x = 0$ at every speed. Mean +discrete |x\_pos| is ≈ 0 cm (s5775) and ≈ 1.0 cm (s42), still at or below the continuous +variant's 0.39–0.66 cm. + +Discretisation also settles faster: mean settle step drops from 184.5 → 73.7 s (s5775) and +168.8 → 74.2 s (s42) going from continuous to discrete. Discretisation does **not**, +however, consistently improve the critic's explained variance in this data — continuous +now edges out discrete on EV for both seeds (0.986–0.987 vs 0.928–0.971), the reverse of +an earlier training run. Position precision and settle speed are the discretisation +benefits that hold robustly; EV is not. --- diff --git a/models/hybrid_cv01_disc_s42.zip b/models/hybrid_cv01_disc_s42.zip index 518a6ec..ffd5b3f 100644 Binary files a/models/hybrid_cv01_disc_s42.zip and b/models/hybrid_cv01_disc_s42.zip differ diff --git a/models/hybrid_cv01_disc_s42_log.csv b/models/hybrid_cv01_disc_s42_log.csv index 7f881f0..04abb00 100644 --- a/models/hybrid_cv01_disc_s42_log.csv +++ b/models/hybrid_cv01_disc_s42_log.csv @@ -1,61 +1,61 @@ t,ep_len_mean,rew_per_step,approx_kl,explained_variance,value_loss,entropy_loss,clip_fraction,policy_gradient_loss,rail_hit_pct,mean_t_min,mean_x_pos_abs,mean_x_vel_abs,mean_energy,mean_theta_dot_abs,mean_theta_dev -50016.0,20.55,-0.5019509678832115,nan,nan,nan,nan,nan,nan,100.0,nan,nan,nan,nan,nan,nan -100032.0,21.75,-0.47805105609195403,nan,nan,nan,nan,nan,nan,100.0,nan,nan,nan,nan,nan,nan 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a/models/hybrid_cv01_disc_s42_play_results.csv b/models/hybrid_cv01_disc_s42_play_results.csv index d55c02b..9231c17 100644 --- a/models/hybrid_cv01_disc_s42_play_results.csv +++ b/models/hybrid_cv01_disc_s42_play_results.csv @@ -1,101 +1,101 @@ start_speed,ep_steps,ep_reward,terminated,truncated,no_crash,t_min_start,t_min_min,t_min_final,t_min_mean_last100,t_min_settle_step,x_pos_final,x_vel_final,theta_final,theta_dot_final,energy_final,acc_final --10.0,1000,-1192.195543353588,False,True,True,0.0,1.5628469735872696e-07,1.0155772695568412e-06,9.88017231078151e-07,110,-2.578492974691926e-14,-2.7755575615628914e-17,3.141592517833754,-1.949877109548935e-07,1.9010103712255745e-12,0.0 --9.8,1000,-1154.176494589993,False,True,True,0.0,2.053983326363068e-07,1.0334575706157872e-06,1.0063920376080572e-06,117,2.6700863742235015e-14,2.7755575615628914e-17,3.141592009383796,-3.4371792435979307e-07,5.907100576214821e-12,0.0 --9.6,1000,-1111.983368870189,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,111,-8.326672684688674e-17,0.0,3.1415913569318783,-8.738476470914778e-08,3.8180485516365596e-13,0.0 --9.4,1000,-1050.539565435699,False,True,True,0.0,1.3328003777005686e-07,1.0161237195664006e-06,9.885790666728205e-07,108,-2.581268532253489e-14,-2.7755575615628914e-17,3.141592419874212,-8.421069873316947e-07,3.545720890587688e-11,0.0 --9.2,1000,-1017.6462659540666,False,True,True,0.0,1.4901161221603232e-07,1.0210286091436453e-06,9.936212760184336e-07,114,-2.606248550307555e-14,-2.7755575615628914e-17,3.141591940510453,7.154032350911054e-07,2.5590089438742412e-11,0.0 --9.0,1000,-962.4044231416215,False,True,True,0.0,7.450580596923828e-08,7.450580596923828e-08,7.450580596923828e-08,102,-1.3877787807814457e-16,0.0,3.141592073017911,-3.036636344737082e-07,4.610580145089094e-12,0.0 --8.8,1000,-935.6095516920404,False,True,True,0.0,1.7631291724481705e-07,1.0172157389243021e-06,9.897017800021423e-07,106,2.5868196473766147e-14,2.7755575615628914e-17,3.141592134609923,-1.0934394517558431e-06,5.978049173250246e-11,0.0 --8.6,1000,-897.8251926268144,False,True,True,0.0,2.28430475821828e-07,1.020484785611662e-06,9.930622961504897e-07,109,-2.603472992745992e-14,-2.7755575615628914e-17,3.1415922891962236,-7.865325662700798e-07,3.093167389038818e-11,0.0 --8.4,1000,-860.2068426787858,False,True,True,0.0,0.0,3.3320009373125275e-08,3.332000937312528e-08,107,2.7755575615628914e-17,0.0,3.1415917701381613,3.0127014946734837e-07,4.538185148003921e-12,0.0 --8.2,1000,-785.4652253581002,False,True,True,0.0,0.0,3.3320009373125275e-08,3.332000937312528e-08,100,2.7755575615628914e-17,0.0,3.1415918690468403,6.752997813877643e-07,2.2801489737118113e-11,0.0 --8.0,1000,-753.4115238437927,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,100,2.7755575615628914e-17,0.0,3.1415919133154624,-3.099488441357866e-07,4.8034142990555064e-12,0.0 --7.8,1000,-688.3497874218359,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,103,-8.326672684688674e-17,0.0,3.1415925733445844,-5.145557320889663e-07,1.3238380071280599e-11,0.0 --7.6,1000,-657.0528346194014,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,93,-8.326672684688674e-17,0.0,3.141591934155311,-7.678798712054865e-07,2.948197483012773e-11,0.0 --7.4,1000,-612.7411128921011,False,True,True,0.0,2.5593584859287915e-07,1.0414835071362951e-06,1.014634113227263e-06,96,2.711719737646945e-14,2.7755575615628914e-17,3.1415924330727454,5.850545512357067e-07,1.7114441396243092e-11,0.0 --7.2,1000,-584.8216616423002,False,True,True,0.0,2.5593584859287915e-07,1.0414835071362951e-06,1.014634113227263e-06,99,2.711719737646945e-14,2.7755575615628914e-17,3.1415925114417753,-9.616410451077601e-07,4.623767498153035e-11,0.0 --7.0,1000,-556.4640449979958,False,True,True,0.0,2.580956830727342e-07,1.0372107362718264e-06,1.010246715851582e-06,91,2.6895152771544417e-14,2.7755575615628914e-17,3.141592012493417,5.5363052858876434e-08,1.5325338110810362e-13,0.0 --6.8,1000,-525.6418891317085,False,True,True,0.0,2.6656007526255796e-07,1.0414835071362951e-06,1.014634113227263e-06,94,2.711719737646945e-14,2.7755575615628914e-17,3.1415923704677304,9.090022936010802e-07,4.131425848885352e-11,0.0 --6.6,1000,-496.86341218517265,False,True,True,0.0,2.235174181852706e-07,1.0356039022535481e-06,1.008596516592447e-06,90,2.681188604469753e-14,2.7755575615628914e-17,3.141592468671246,-5.991368726174799e-07,1.7948249606326417e-11,0.0 --6.4,1000,-472.66755103272044,False,True,True,0.0,2.580956830727342e-07,1.0356039022535481e-06,1.008596516592447e-06,94,2.681188604469753e-14,2.7755575615628914e-17,3.141591908382737,5.251255553482498e-07,1.378784244413614e-11,0.0 --6.2,1000,-427.23659365300375,False,True,True,0.0,2.6656007526255796e-07,1.0452078534011122e-06,1.0184575668121501e-06,98,2.731148640577885e-14,2.7755575615628914e-17,3.141592296894463,-1.2516298989237714e-07,7.832887019052254e-13,0.0 --6.0,1000,-402.59124455392606,False,True,True,0.0,2.6656007526255796e-07,1.0452078534011122e-06,1.0184575668121501e-06,98,2.731148640577885e-14,2.7755575615628914e-17,3.1415922287106763,-6.00097183035927e-08,1.8005831452717141e-13,0.0 --5.8,1000,-361.0619860762978,False,True,True,0.0,1.8848643689304542e-07,1.036139790466373e-06,1.0091468831400384e-06,84,2.683964162031316e-14,2.7755575615628914e-17,3.1415926237874707,6.889886461375324e-07,2.3735267725512727e-11,0.0 --5.6,1000,-326.68272713888217,False,True,True,0.0,1.6989937826421605e-07,1.0334575706157872e-06,1.0063920376080572e-06,78,2.6700863742235015e-14,2.7755575615628914e-17,3.1415924227419967,4.695810570048303e-07,1.1025318455019019e-11,0.0 --5.4,1000,-296.3896745422127,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,76,8.326672684688674e-17,0.0,3.1415920745497656,2.2328321036126272e-07,2.4927696014615953e-12,0.0 --5.2,1000,-273.48660790670095,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,77,8.326672684688674e-17,0.0,3.14159255035152,-7.546234890081236e-07,2.8472830508139685e-11,0.0 --5.0,1000,-244.45257395351825,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,81,8.326672684688674e-17,0.0,3.1415925886371374,-2.6908046839806337e-07,3.620214923666059e-12,0.0 --4.8,1000,-215.02715751236582,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,72,-2.7755575615628914e-17,0.0,3.1415925536297653,3.4620543404714944e-07,5.992910128188757e-12,0.0 --4.6,1000,-194.69411817450498,False,True,True,0.0,3.3320009373125275e-08,6.664001874625055e-08,6.664001874625056e-08,76,-1.1102230246251565e-16,0.0,3.141592293795401,-1.447047291182691e-07,1.0469729314595816e-12,0.0 --4.4,1000,-174.49200132422774,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,69,-2.7755575615628914e-17,0.0,3.1415924011481136,5.4746140219764655e-08,1.4985699344810668e-13,0.0 --4.2,1000,-156.17316344790765,False,True,True,0.0,3.3320009373125275e-08,5.7711949142924205e-08,5.771194914292422e-08,63,-8.326672684688674e-17,0.0,3.1415925978347534,5.214058763862874e-07,1.3593204396507618e-11,0.0 --4.0,1000,-135.6781632855719,False,True,True,0.0,0.0,0.0,0.0,63,0.0,0.0,3.141592241444039,2.410762424965168e-07,2.905887734811969e-12,0.0 --3.8,1000,-118.23486270574901,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,65,-2.7755575615628914e-17,0.0,3.1415926141650266,-7.01848078114949e-07,2.4629536237682375e-11,0.0 --3.6,1000,-100.786433328022,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,56,-2.7755575615628914e-17,0.0,3.1415922342339453,2.881471823244588e-07,4.151439934076246e-12,0.0 --3.4,1000,-87.62479619664981,False,True,True,0.0,1.632340426553752e-07,1.0334575706157872e-06,1.0063920376080572e-06,71,2.6700863742235015e-14,2.7755575615628914e-17,3.1415926325163688,-2.802847285512839e-07,3.927976452875551e-12,0.0 --3.2,1000,-73.46362694429781,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,53,2.7755575615628914e-17,0.0,3.1415926041682325,4.7674017660620276e-07,1.136405979952567e-11,0.0 --3.0,1000,-59.46267878248247,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,51,-2.7755575615628914e-17,0.0,3.141592383718286,-4.737890618189259e-07,1.1223803754962899e-11,0.0 --2.8,1000,-49.3165173360185,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,48,-2.7755575615628914e-17,0.0,3.1415925391318025,-4.6899539596528965e-07,1.0997834071831942e-11,0.0 --2.6,1000,-40.43237926012979,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,58,-2.7755575615628914e-17,0.0,3.141592463928975,-1.0840708173969985e-07,5.876047685658982e-13,0.0 --2.4,1000,-32.95681797881055,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,52,-2.7755575615628914e-17,0.0,3.1415925679891394,-5.474272631054567e-07,1.4983830419556547e-11,0.0 --2.2,1000,-25.942326062032723,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,40,-2.7755575615628914e-17,0.0,3.1415922438755985,3.176867071525118e-07,5.046242195070289e-12,0.0 --2.0,1000,-20.166707764404244,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,39,2.7755575615628914e-17,0.0,3.1415924810963816,2.289105235289303e-07,2.620001389114448e-12,0.0 --1.8,1000,-14.385530859702763,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,41,-8.326672684688674e-17,0.0,3.1415924804539457,2.0655724446487277e-08,2.1332947620460608e-14,0.0 --1.6,1000,-10.340282058475307,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,31,-2.7755575615628914e-17,0.0,3.141592258217308,-7.123541179212562e-08,2.5372419465968557e-13,0.0 --1.4,1000,-7.14206906253417,False,True,True,0.0,0.0,3.3320009373125275e-08,3.332000937312528e-08,43,-2.7755575615628914e-17,0.0,3.141592271352498,2.730542619319538e-07,3.727931497960201e-12,0.0 --1.2,1000,-4.565064608288041,False,True,True,0.0,0.0,3.3320009373125275e-08,3.332000937312528e-08,23,-2.7755575615628914e-17,0.0,3.1415925939851483,2.8755816942070887e-07,4.1344850400294545e-12,0.0 --1.0,1000,-2.7921190317200013,False,True,True,0.0,0.0,0.0,0.0,26,0.0,0.0,3.1415924836904154,-1.323825931417327e-07,8.762575483464768e-13,0.0 --0.8,1000,-1.5631222886885245,False,True,True,0.0,0.0,0.0,0.0,19,0.0,0.0,3.1415924680718326,2.9997493243832994e-08,4.49924800456903e-14,0.0 --0.6,1000,-0.9067375862983912,False,True,True,0.0,0.0,0.0,0.0,15,0.0,0.0,3.1415925343805036,2.939013974527409e-07,4.318901571233699e-12,0.0 --0.4,1000,-0.41702400356121444,False,True,True,0.0,0.0,0.0,0.0,13,0.0,0.0,3.1415924509120887,-2.8838487331584587e-07,4.158291757869823e-12,0.0 --0.2,1000,-0.12276464125646784,False,True,True,0.0,0.0,0.0,0.0,6,0.0,0.0,3.1415926325163688,1.7197668506065976e-07,1.4787990102226677e-12,0.0 -0.2,1000,-0.18106588375490226,False,True,True,0.0,0.0,0.0,0.0,7,0.0,0.0,3.141592483038208,1.8578511974639808e-07,1.7258055359591735e-12,0.0 -0.4,1000,-0.3192316872483298,False,True,True,0.0,0.0,0.0,0.0,17,0.0,0.0,3.1415925886371374,1.299124271879697e-07,8.438619368934766e-13,0.0 -0.6,1000,-0.7449565904837023,False,True,True,0.0,0.0,0.0,0.0,14,0.0,0.0,3.141592627780225,-1.9083973119224234e-07,1.820990150076366e-12,0.0 -0.8,1000,-1.713722789854182,False,True,True,0.0,0.0,0.0,0.0,33,0.0,0.0,3.1415924509120887,-2.701870408334908e-07,3.650051851717922e-12,0.0 -1.0,1000,-2.885308731517347,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,47,-2.7755575615628914e-17,0.0,3.141592444442029,-1.432038373696367e-08,1.0253669518694676e-14,0.0 -1.2,1000,-4.4102817135760715,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,23,-2.7755575615628914e-17,0.0,3.1415925733445844,-2.2898084547894704e-08,2.621611379812671e-14,0.0 -1.4,1000,-7.063977110041446,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,38,2.7755575615628914e-17,0.0,3.1415924735383776,-4.533065238925357e-08,1.0274340230176703e-13,0.0 -1.6,1000,-10.03219516340965,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,31,2.7755575615628914e-17,0.0,3.1415922237745875,5.497637762201822e-08,1.5112010482193727e-13,0.0 -1.8,1000,-14.820567496093437,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,37,-8.326672684688674e-17,0.0,3.141592579083987,6.428357959321835e-08,2.0661893026588196e-13,0.0 -2.0,1000,-19.734494839926867,False,True,True,0.0,1.5628469735872696e-07,1.0425489636879214e-06,1.0157279985711213e-06,43,2.7172708527700706e-14,2.7755575615628914e-17,3.141592376011916,-3.3752896127053197e-07,5.696289984724531e-12,0.0 -2.2,1000,-28.790649621108535,False,True,True,0.0,1.4901161221603232e-07,1.0398832754507371e-06,1.0129910682350428e-06,70,2.7033930649622562e-14,2.7755575615628914e-17,3.141592409647271,-2.0307242483264396e-07,2.0619204863141276e-12,0.0 -2.4,1000,-32.61099086167297,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,42,-2.7755575615628914e-17,0.0,3.1415925761610883,1.1678678156783471e-07,6.819576174486569e-13,0.0 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+3000960.0,1000.0,-0.0007998911799999998,0.0003889442014042288,0.9713470935821533,2.26329641136086e-05,-0.0025242651503575076,0.001551055908203125,-3.4363198046349905e-05,0.0,4.346088179103297e-09,3.620292471603771e-18,0.0,1.575776996422616e-12,1.592114925086505e-07,1.6959289719424744e-07 diff --git a/models/hybrid_cv01_disc_s5775_meta.json b/models/hybrid_cv01_disc_s5775_meta.json index 5d94688..8ad86cd 100644 --- a/models/hybrid_cv01_disc_s5775_meta.json +++ b/models/hybrid_cv01_disc_s5775_meta.json @@ -18,7 +18,7 @@ "n_envs": 32, "gamma": 0.99, "save_path": "models/hybrid_cv01_disc_s5775.zip", - "seed": null, + "seed": 5775, "ent_coef": 0.0, "learning_rate": 0.0003, "clip_range": 0.2, @@ -28,7 +28,9 @@ "start_speed": 1.0, "continuous_actions": false, "reward_limit": 50.0, - "max_episode_steps": 1000 + "log_std_init": null, + "max_episode_steps": 1000, + "acc": 0.1 }, "config_source": "experiments/hybrid_cv01_disc_s5775.yaml" } \ No newline at end of file diff --git a/models/hybrid_cv01_disc_s5775_play_results.csv b/models/hybrid_cv01_disc_s5775_play_results.csv index 3e84c1f..c4f41ee 100644 --- a/models/hybrid_cv01_disc_s5775_play_results.csv +++ b/models/hybrid_cv01_disc_s5775_play_results.csv @@ -1,101 +1,101 @@ start_speed,ep_steps,ep_reward,terminated,truncated,no_crash,t_min_start,t_min_min,t_min_final,t_min_mean_last100,t_min_settle_step,x_pos_final,x_vel_final,theta_final,theta_dot_final,energy_final,acc_final --10.0,1000,-1216.9624407806393,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,112,2.7755575615628914e-17,0.0,3.1415925053251113,-9.89284638363903e-07,4.8934204785139904e-11,0.0 --9.8,1000,-1154.108442802568,False,True,True,0.0,4.7121609153872417e-08,4.7121609153872417e-08,4.712160915387242e-08,116,-5.551115123125783e-17,0.0,3.141591293823113,-1.4025055802948436e-07,9.835109513790879e-13,0.0 --9.6,1000,-1111.3098304062364,False,True,True,0.0,7.450580596923828e-08,9.996002811937583e-08,9.996002811937583e-08,107,-2.498001805406602e-16,0.0,3.141591752223492,1.1644378346472869e-07,6.779577353790312e-13,0.0 --9.4,1000,-1047.5966693177877,False,True,True,0.0,1.8551796103440497e-07,1.0133885228977154e-06,9.857666830631602e-07,126,-2.5673907444456745e-14,-2.7755575615628914e-17,3.141591609976461,-4.1404495257722765e-08,8.571661138883139e-14,0.0 --9.2,1000,-1006.6309582805516,False,True,True,0.0,1.1050996910178159e-07,1.1050996910178159e-07,1.1050996910178164e-07,106,3.0531133177191805e-16,0.0,3.1415922430634784,-9.224864108604274e-07,4.2549058911107666e-11,0.0 --9.0,1000,-979.5120742695048,False,True,True,0.0,9.996002811937583e-08,9.996002811937583e-08,9.996002811937583e-08,105,2.498001805406602e-16,0.0,3.1415923804469967,-5.181583534262098e-08,1.3424403961268047e-13,0.0 --8.8,1000,-947.5299598600016,False,True,True,0.0,0.0,0.0,0.0,110,0.0,0.0,3.141592114464216,8.734317488940197e-09,3.814415099880329e-15,0.0 --8.6,1000,-883.1807864586158,False,True,True,0.0,4.7121609153872417e-08,4.7121609153872417e-08,4.712160915387242e-08,102,5.551115123125783e-17,0.0,3.1415924531151727,-7.66647593733388e-07,2.9387426648859693e-11,0.0 --8.4,1000,-855.1075404208059,False,True,True,0.0,4.7121609153872417e-08,4.7121609153872417e-08,4.712160915387242e-08,102,5.551115123125783e-17,0.0,3.1415918969980994,-5.854437812121744e-07,1.7137221048000415e-11,0.0 --8.2,1000,-794.3006987312361,False,True,True,0.0,7.450580596923828e-08,7.450580596923828e-08,7.450580596923828e-08,98,-1.3877787807814457e-16,0.0,3.1415918911510903,3.641917566003959e-07,6.631781778784102e-12,0.0 --8.0,1000,-766.5668870867513,False,True,True,0.0,7.450580596923828e-08,8.815645848363065e-08,8.815645848363065e-08,99,-1.942890293094024e-16,0.0,3.1415923677359587,7.336160992887986e-08,2.6909629056785624e-13,0.0 --7.8,1000,-733.0651950697148,False,True,True,0.0,8.815645848363065e-08,1.0536712127723508e-07,1.0536712127723512e-07,101,-2.7755575615628914e-16,0.0,3.1415917685059744,-6.825956900628347e-07,2.3296843804617875e-11,0.0 --7.6,1000,-654.1651017806802,False,True,True,0.0,0.0,5.7711949142924205e-08,5.771194914292422e-08,98,-8.326672684688674e-17,0.0,3.1415918398717757,-2.8955269923140616e-07,4.192038281609658e-12,0.0 --7.4,1000,-626.8775318173011,False,True,True,0.0,0.0,3.3320009373125275e-08,3.332000937312528e-08,96,2.7755575615628914e-17,0.0,3.14159238996134,-6.617246473581618e-07,2.1893975446064185e-11,0.0 --7.2,1000,-562.1365927370937,False,True,True,0.0,0.0,0.0,0.0,92,0.0,0.0,3.1415921894957655,-3.857490659702845e-07,7.440117094847345e-12,0.0 --7.0,1000,-535.3133762933028,False,True,True,0.0,0.0,0.0,0.0,88,0.0,0.0,3.141592653589793,9.202891752062502e-07,4.234660830009001e-11,0.0 --6.8,1000,-504.0399014354889,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,95,-2.7755575615628914e-17,0.0,3.1415918503080107,-5.198199399177165e-07,1.351063849680292e-11,0.0 --6.6,1000,-473.1812038417217,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,98,-2.7755575615628914e-17,0.0,3.141592533452791,-2.2141025525382565e-07,2.4511250565782114e-12,0.0 --6.4,1000,-440.1949757166584,False,True,True,0.0,2.332400658894327e-07,1.0345312930563924e-06,1.0074948806494595e-06,97,2.6756374893466273e-14,2.7755575615628914e-17,3.1415925325321874,-2.644994616198793e-07,3.497998259786886e-12,0.0 --6.2,1000,-415.52194689497605,False,True,True,0.0,2.2598729888691832e-07,1.0339945712081895e-06,1.0069436102643404e-06,106,2.6728619317850644e-14,2.7755575615628914e-17,3.1415920556836014,6.90352006405593e-07,2.3829294637603004e-11,0.0 --6.0,1000,-379.33978506389775,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,90,-2.7755575615628914e-17,0.0,3.141592392075476,-2.5889089523724644e-07,3.3512247818371452e-12,0.0 --5.8,1000,-354.33779494864035,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,81,2.7755575615628914e-17,0.0,3.141591838372336,2.1279752081057948e-07,2.2641392431564505e-12,0.0 --5.6,1000,-330.3580328324902,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,80,2.7755575615628914e-17,0.0,3.1415923570604294,7.48484421430958e-07,2.8011446456241797e-11,0.0 --5.4,1000,-307.2569856498099,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,78,2.7755575615628914e-17,0.0,3.141591821789944,5.990843281634304e-07,1.7945101612551435e-11,0.0 --5.2,1000,-279.62868097961626,False,True,True,0.0,2.1593834109330013e-07,1.0350677365940133e-06,1.0080458492603154e-06,77,2.67841304690819e-14,2.7755575615628914e-17,3.1415922543054005,-6.68223286875788e-07,2.2326118055968617e-11,0.0 --5.0,1000,-257.24249978227846,False,True,True,0.0,1.8250120777699858e-07,1.031844892020266e-06,1.0047355010226889e-06,82,2.6617597015388128e-14,2.7755575615628914e-17,3.141592409647271,2.455460149028213e-07,3.0146422718009802e-12,0.0 --4.8,1000,-217.15752213562058,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,75,-2.7755575615628914e-17,0.0,3.1415918398717757,-1.1322502306074415e-07,6.409952923553022e-13,0.0 --4.6,1000,-194.7409739738007,False,True,True,0.0,3.3320009373125275e-08,5.7711949142924205e-08,5.771194914292422e-08,77,-8.326672684688674e-17,0.0,3.1415922355597656,-5.880527466010705e-07,1.729030163925314e-11,0.0 --4.4,1000,-172.95007514076903,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,70,-2.7755575615628914e-17,0.0,3.1415922554191664,-1.167015474792467e-07,6.809625592025435e-13,0.0 --4.2,1000,-154.47499874467644,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,62,-2.7755575615628914e-17,0.0,3.1415920446444012,5.737092756509747e-07,1.64571166483983e-11,0.0 --4.0,1000,-128.5233620796654,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,64,-2.7755575615628914e-17,0.0,3.1415919514936297,-2.0622425146767343e-07,2.1264220946701103e-12,0.0 --3.8,1000,-112.57794554731723,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,57,-2.7755575615628914e-17,0.0,3.1415922593421257,-3.508003961619052e-07,6.1530458973674815e-12,0.0 --3.6,1000,-97.8165011724718,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,64,-2.7755575615628914e-17,0.0,3.1415925186540394,-2.0391660665240197e-07,2.0790991234315216e-12,0.0 --3.4,1000,-84.32903164178482,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,57,-2.7755575615628914e-17,0.0,3.141592369293757,-4.372229108571112e-07,9.558193688918272e-12,0.0 --3.2,1000,-70.4762079732039,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,59,-2.7755575615628914e-17,0.0,3.1415925211453755,-2.348123110073994e-07,2.7568410700317832e-12,0.0 --3.0,1000,-59.179892480247666,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,44,-2.7755575615628914e-17,0.0,3.1415924863246736,4.3555329027386383e-07,9.485333433419435e-12,0.0 --2.8,1000,-49.3165173360185,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,48,-2.7755575615628914e-17,0.0,3.1415925391318025,-4.6899539596528965e-07,1.0997834071831942e-11,0.0 --2.6,1000,-39.81975849236592,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,42,-2.7755575615628914e-17,0.0,3.141592579083987,-1.5380745834240853e-07,1.1828367120875866e-12,0.0 --2.4,1000,-32.28175010596725,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,36,-2.7755575615628914e-17,0.0,3.1415922529175466,3.630234938186498e-07,6.589302853214965e-12,0.0 --2.2,1000,-25.642907615726113,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,37,-2.7755575615628914e-17,0.0,3.141592211549917,-8.350649482614143e-08,3.486667339074193e-13,0.0 --2.0,1000,-20.208718797685084,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,51,-8.326672684688674e-17,0.0,3.1415924760219642,-4.5957099206735244e-07,1.0560274837488527e-11,0.0 --1.8,1000,-14.897118861003912,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,34,-8.326672684688674e-17,0.0,3.1415922916418455,2.6375587802980037e-07,3.478358159763547e-12,0.0 --1.6,1000,-10.496569641563728,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,30,-8.326672684688674e-17,0.0,3.1415925451077023,-1.2576476564251733e-07,7.908388138558655e-13,0.0 --1.4,1000,-7.423115337537607,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,27,-8.326672684688674e-17,0.0,3.1415924987323836,1.649914758915253e-07,1.3611093558431888e-12,0.0 --1.2,1000,-4.565064608288041,False,True,True,0.0,0.0,3.3320009373125275e-08,3.332000937312528e-08,23,-2.7755575615628914e-17,0.0,3.1415925939851483,2.8755816942070887e-07,4.1344850400294545e-12,0.0 --1.0,1000,-2.767570823938208,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,28,2.7755575615628914e-17,0.0,3.1415925679891394,-8.42182526098947e-08,3.5463570363320175e-13,0.0 --0.8,1000,-1.6780305855939208,False,True,True,0.0,0.0,0.0,0.0,19,0.0,0.0,3.1415922681707475,-1.1871455159350491e-07,7.046572380023469e-13,0.0 --0.6,1000,-0.8247662787729351,False,True,True,0.0,0.0,0.0,0.0,13,0.0,0.0,3.141592358938405,9.5872892856184e-08,4.595805792306669e-13,0.0 --0.4,1000,-0.32908884478933315,False,True,True,0.0,0.0,0.0,0.0,20,0.0,0.0,3.1415926325163688,-1.324962853994415e-07,8.777632822325128e-13,0.0 --0.2,1000,-0.24782102601673875,False,True,True,0.0,0.0,0.0,0.0,22,0.0,0.0,3.141592531618532,-7.496908545091516e-08,2.8101818866733093e-13,0.0 -0.2,1000,-0.18106588375490226,False,True,True,0.0,0.0,0.0,0.0,7,0.0,0.0,3.141592483038208,1.8578511974639808e-07,1.7258055359591735e-12,0.0 -0.4,1000,-0.3192316872483298,False,True,True,0.0,0.0,0.0,0.0,17,0.0,0.0,3.1415925886371374,1.299124271879697e-07,8.438619368934766e-13,0.0 -0.6,1000,-0.7449565904837023,False,True,True,0.0,0.0,0.0,0.0,14,0.0,0.0,3.141592627780225,-1.9083973119224234e-07,1.820990150076366e-12,0.0 -0.8,1000,-1.588382502893819,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,17,-2.7755575615628914e-17,0.0,3.141592464515254,-1.8958431051227046e-07,1.7971105396206493e-12,0.0 -1.0,1000,-2.827411633795834,False,True,True,0.0,0.0,0.0,0.0,18,0.0,0.0,3.1415925091176753,-3.4845844435550206e-07,6.071164372132826e-12,0.0 -1.2,1000,-4.5994679222009385,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,22,-2.7755575615628914e-17,0.0,3.1415923849552896,-1.798779920386028e-07,1.6178046009919829e-12,0.0 -1.4,1000,-6.899973530536978,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,26,-2.7755575615628914e-17,0.0,3.1415924843451357,2.7343036520921024e-07,3.7382082309221045e-12,0.0 -1.6,1000,-10.633495854545433,False,True,True,0.0,0.0,0.0,0.0,52,0.0,0.0,3.1415925733445844,-2.2886533900486633e-07,2.6189671698906193e-12,0.0 -1.8,1000,-14.373733155932646,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,38,8.326672684688674e-17,0.0,3.141592528917734,-1.28306907724705e-07,8.23133128493798e-13,0.0 -2.0,1000,-19.276060817528506,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,35,-2.7755575615628914e-17,0.0,3.1415924633445025,1.3107115992746973e-08,8.589824482366174e-15,0.0 -2.2,1000,-24.67378719214774,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,41,8.326672684688674e-17,0.0,3.1415924836904154,1.256195906044484e-07,7.89014077181461e-13,0.0 -2.4,1000,-31.29439072882564,False,True,True,0.0,5.7711949142924205e-08,5.7711949142924205e-08,5.771194914292422e-08,35,8.326672684688674e-17,0.0,3.1415926019706566,-1.5873478450408618e-07,1.2598365905779339e-12,0.0 -2.6,1000,-40.12568914898011,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,42,-2.7755575615628914e-17,0.0,3.1415925254051578,4.4342074528224355e-09,9.831097867333015e-16,0.0 -2.8,1000,-49.3165173360185,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,48,2.7755575615628914e-17,0.0,3.1415925391318025,4.6899539596528965e-07,1.0997834071831942e-11,0.0 -3.0,1000,-60.17061084343672,False,True,True,0.0,1.5979715158626526e-07,1.0425489636879214e-06,1.0157279985711213e-06,51,-2.7172708527700706e-14,-2.7755575615628914e-17,3.1415925570192313,3.31834518250575e-07,5.5057073750374575e-12,0.0 -3.2,1000,-70.56661261967442,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,59,2.7755575615628914e-17,0.0,3.1415920818821244,-2.2600027167093296e-07,2.553806139766775e-12,0.0 -3.4,1000,-83.85547822774338,False,True,True,0.0,3.3320009373125275e-08,6.664001874625055e-08,6.664001874625056e-08,61,-1.1102230246251565e-16,0.0,3.141592551432579,6.582301952270354e-07,2.1663349495431052e-11,0.0 -3.6,1000,-97.8863446812854,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,61,2.7755575615628914e-17,0.0,3.1415924850023993,4.904824678084189e-07,1.202865256137183e-11,0.0 -3.8,1000,-112.65663358361908,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,60,2.7755575615628914e-17,0.0,3.1415920642870168,1.879716481653069e-07,1.7666670256990961e-12,0.0 -4.0,1000,-135.60345009099203,False,True,True,0.0,1.8250120777699858e-07,1.036675401662749e-06,1.0096969493959209e-06,62,-2.6867397195928788e-14,-2.7755575615628914e-17,3.14159235445107,-5.499383735489683e-07,1.5121610735236868e-11,0.0 -4.2,1000,-154.47499874467644,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,62,2.7755575615628914e-17,0.0,3.1415920446444012,-5.737092756509747e-07,1.64571166483983e-11,0.0 -4.4,1000,-172.90566945251626,False,True,True,0.0,3.3320009373125275e-08,3.3320009373125275e-08,3.332000937312528e-08,66,2.7755575615628914e-17,0.0,3.1415920942501394,-5.119246163558659e-07,1.3103340641555024e-11,0.0 -4.6,1000,-195.73101239453538,False,True,True,0.0,3.3320009373125275e-08,5.7711949142924205e-08,5.771194914292422e-08,74,-8.326672684688674e-17,0.0,3.141592583697152,5.860361704603166e-07,1.717191965438966e-11,0.0 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a/models/hybrid_cv01_s42_meta.json +++ b/models/hybrid_cv01_s42_meta.json @@ -18,7 +18,7 @@ "n_envs": 32, "gamma": 0.99, "save_path": "models/hybrid_cv01_s42.zip", - "seed": null, + "seed": 42, "ent_coef": 0.0, "learning_rate": 0.0003, "clip_range": 0.2, @@ -28,7 +28,9 @@ "start_speed": 1.0, "continuous_actions": true, "reward_limit": 50.0, - "max_episode_steps": 1000 + "log_std_init": -3.2188758248682006, + "max_episode_steps": 1000, + "acc": 0.1 }, "config_source": "experiments/hybrid_cv01_s42.yaml" } \ No newline at end of file diff --git a/models/hybrid_cv01_s42_play_results.csv b/models/hybrid_cv01_s42_play_results.csv index 8d2a0f0..e64ebcd 100644 --- a/models/hybrid_cv01_s42_play_results.csv +++ b/models/hybrid_cv01_s42_play_results.csv @@ -1,101 +1,101 @@ start_speed,ep_steps,ep_reward,terminated,truncated,no_crash,t_min_start,t_min_min,t_min_final,t_min_mean_last100,t_min_settle_step,x_pos_final,x_vel_final,theta_final,theta_dot_final,energy_final,acc_final --10.0,1000,-1180.0692349905542,False,True,True,0.0,0.11461912521750128,0.4512612177217308,0.451261052970065,159,0.005090916156844993,4.470348439118103e-09,3.141592653589793,-1.449622828278031e-09,5.025913331581859e-17,2.2351741790771488e-09 --9.8,1000,-1135.7245771898738,False,True,True,0.0,0.05055469319014679,0.4512610952906458,0.4512610810030388,153,0.005090915411719929,-4.470348349617903e-09,3.141592653589793,9.305384581582844e-10,1.1688787683709086e-17,-1.2665987014770509e-08 --9.6,1000,-1093.6946603911438,False,True,True,0.0,0.06286490708589561,0.4512610814021839,0.45126105993365256,166,0.005090913921598854,7.4505806773379e-10,3.141592653589793,-2.6342745181347076e-10,1.7845693990047e-18,8.195638656616212e-09 --9.4,1000,-1052.9980628309172,False,True,True,0.0,0.021495824945671944,0.4512608087670558,0.4512610955942708,153,0.005090909451197094,-6.705522587645474e-09,3.141592653589793,1.3977608795058085e-09,2.644161890353733e-17,-3.725290298461914e-09 --9.2,1000,-1007.2492017997598,False,True,True,0.0,0.1216949752063572,0.4512610079108123,0.45126104035605286,139,0.005090912431517046,4.05222125337815e-17,3.141592653589793,1.1061027014885084e-09,6.117315976292267e-17,-3.725290298461914e-09 --9.0,1000,-953.6747985338292,False,True,True,0.0,0.06767551600933085,0.45126084602173683,0.45126105369128455,152,0.005090909451237382,-2.9802322480553204e-09,3.141592653589793,1.2563927159142406e-09,4.5923604070290076e-17,-3.725290298461914e-09 --8.8,1000,-907.2860777596871,False,True,True,0.0,0.08172047362866984,0.4512614498783789,0.4512610697745776,139,0.005090919882038258,1.1175870872033492e-08,3.141592653589793,-2.2690994414948742e-10,3.966528865733702e-17,1.1175870895385742e-08 --8.6,1000,-863.3745272802244,False,True,True,0.0,0.023169063031673365,0.45126091951540886,0.4512610760820126,159,0.00509091094137067,-2.235174171638589e-09,3.141592653589793,-8.93096130927635e-10,6.234129078884203e-17,-7.450580596923829e-10 --8.4,1000,-820.6072476214401,False,True,True,0.0,0.06796220495850269,0.4512607533973487,0.4512610335819078,138,0.005090908706211132,-8.940696693027954e-09,3.141592653589793,1.9971284802416264e-09,6.083693701527249e-17,-1.564621925354004e-08 --8.2,1000,-784.2080871527563,False,True,True,0.0,0.0793360081639154,0.4512610548186997,0.4512610803424086,145,0.00509091466665697,-5.215406412448348e-09,3.141592653589793,1.1108410751733562e-09,1.7363750072317532e-17,-9.685754776000977e-09 --8.0,1000,-746.2050614051963,False,True,True,0.0,0.057515785486189706,0.451261184695787,0.4512611100599196,139,0.0050909154116787385,4.470348324794005e-09,3.141592653589793,-2.1455861975146695e-10,2.7022594747440623e-18,2.2351741790771488e-09 --7.8,1000,-702.5176050891505,False,True,True,0.0,0.07990532248326815,0.4512611176410038,0.45126109090807803,139,0.005090915411688789,-2.2351742019667964e-09,3.141592653589793,6.895898876617492e-10,1.0861177246408362e-17,-7.450580596923829e-10 --7.6,1000,-656.7035393931666,False,True,True,0.0,0.09714226981229467,0.4512610920719363,0.4512610052881291,138,0.00509091466666462,-1.4901161080521121e-09,3.141592653589793,2.16252110589082e-09,2.0271102434583424e-16,-3.725290298461914e-09 --7.4,1000,-612.9434214033337,False,True,True,0.0,0.03238297806275611,0.45126112085822717,0.45126112759220804,154,0.005090916156710606,-5.2154064777979045e-09,3.141592653589793,2.0466802029229732e-10,5.02041310386322e-18,-1.564621925354004e-08 --7.2,1000,-579.2995205725862,False,True,True,0.0,0.028990136273650328,0.4512607682995351,0.4512610714606435,145,0.00509090870623438,-7.450580547165677e-09,3.141592653589793,1.145489437490133e-09,8.017264614221974e-18,-3.725290298461914e-09 --7.0,1000,-530.6885727007948,False,True,True,0.0,0.07302232167320348,0.451260931195518,0.45126101340409375,147,0.005090910196262759,2.235174136225882e-09,3.141592653589793,-1.2913997049729224e-09,5.701862941079607e-17,2.2351741790771488e-09 --6.8,1000,-495.4356200048267,False,True,True,0.0,0.018853895366191985,0.4512610046897116,0.45126108509341156,133,0.005090911686407881,2.980232225814726e-09,3.141592653589793,-6.01365018923385e-10,4.600812270039421e-18,2.2351741790771488e-09 --6.6,1000,-464.5211793154341,False,True,True,0.0,0.06736448514204452,0.4512608907239719,0.4512610312357126,146,0.005090909451209253,1.4901160804087343e-09,3.141592653589793,9.051875685942218e-10,5.556679519666574e-17,-7.450580596923829e-10 --6.4,1000,-435.375202646805,False,True,True,0.0,0.09074283905077324,0.4512611506598717,0.45126107901829365,120,0.0050909161566954275,-2.235174255010023e-09,3.141592653589793,1.8574328367024944e-10,7.134428950510071e-20,-7.450580596923829e-10 --6.2,1000,-410.18004310344116,False,True,True,0.0,0.06894115943858509,0.45126112610626906,0.4512610143006473,127,0.005090913921612293,5.215406437291012e-09,3.141592653589793,-3.02314902408651e-09,3.129022244297672e-16,1.4156103134155274e-08 --6.0,1000,-375.93353556136253,False,True,True,0.0,0.114407151371529,0.451261099520203,0.4512611011760486,105,0.005090914666612431,-7.450581011191511e-10,3.141592653589793,1.5078625549333035e-09,1.0272557789577358e-16,-3.725290298461914e-09 --5.8,1000,-347.9524710842591,False,True,True,0.0,0.0757095724604181,0.45126111662650026,0.4512610922917736,126,0.005090916901769101,-8.940696775422012e-09,3.141592653589793,1.6045425553428567e-09,2.5238585501763478e-17,-2.1606683731079104e-08 --5.6,1000,-320.9623781070679,False,True,True,0.0,0.02966550557873494,0.4512610143473784,0.4512610714592581,126,0.005090913921608153,-5.960464462239889e-09,3.141592653589793,3.092415106157891e-10,4.112853554557569e-18,-1.2665987014770509e-08 --5.4,1000,-292.5552369585959,False,True,True,0.0,0.10852332254452246,0.4512609930104966,0.45126109625916877,113,0.005090912431536108,-1.4901160587536814e-09,3.141592653589793,1.699367725964706e-09,1.2018020496160814e-16,-7.450580596923829e-10 --5.2,1000,-267.91350487137544,False,True,True,0.0,0.06258513095664625,0.45126101011696157,0.4512610532023244,119,0.005090914666696009,-9.685754730727423e-09,3.141592653589793,1.4617877038755875e-09,1.2162915232400596e-17,-1.862645149230957e-08 --5.0,1000,-244.7732258614831,False,True,True,0.0,0.11261412675386992,0.4512607320582694,0.4512610655800464,118,0.005090907216090413,-4.470348339239417e-09,3.141592653589793,7.341593807443897e-10,4.122025270320186e-18,-3.725290298461914e-09 --4.8,1000,-217.20286281170672,False,True,True,0.0,0.06633456508684603,0.45126162565503547,0.451261050197087,119,0.005090924352423764,8.940696733639263e-09,3.141592653589793,-7.321361447195461e-10,1.3111233844260208e-18,1.1175870895385742e-08 --4.6,1000,-190.55879294225053,False,True,True,0.0,0.05387966400859802,0.4512608748128725,0.4512610270423158,120,0.00509091094139184,-6.705522506410637e-09,3.141592653589793,1.4941860564641437e-09,3.391863230663104e-17,-1.2665987014770509e-08 --4.4,1000,-168.0489643066495,False,True,True,0.0,0.09299948066472942,0.4512607427240551,0.451261053028532,114,0.005090907961066089,-6.705522603468161e-09,3.141592653589793,1.1263934892286221e-09,1.0389561298079035e-17,-1.2665987014770509e-08 --4.2,1000,-148.40144418069943,False,True,True,0.0,0.08435881007226394,0.45126107818286815,0.45126107020193823,108,0.005090913176529826,3.725290292402327e-09,3.141592653589793,-2.0094088309867768e-09,1.3396877426848577e-16,2.2351741790771488e-09 --4.0,1000,-130.43825021804665,False,True,True,0.0,0.07786677401769299,0.45126104194057504,0.4512610744603181,114,0.005090911686361694,6.705522476298435e-09,3.141592653589793,-4.604236598361167e-10,2.207701171231238e-18,8.195638656616212e-09 --3.8,1000,-113.86699563426257,False,True,True,0.0,0.08420591539238197,0.45126112187450174,0.4512610641304072,106,0.005090914666670102,1.4901161381424075e-09,3.141592653589793,-1.10607272943867e-09,4.5798298952230414e-17,2.2351741790771488e-09 --3.6,1000,-99.16772955422829,False,True,True,0.0,0.05456880423002656,0.4512613018799847,0.45126110153954,107,0.005090918391928709,2.980232224018624e-09,3.141592653589793,-9.096887838423793e-10,1.8706737951520848e-17,2.2351741790771488e-09 --3.4,1000,-85.0367106491768,False,True,True,0.0,0.050249826515675715,0.4512611953659471,0.4512610140012642,95,0.005090916156753892,2.235174163346722e-09,3.141592653589793,3.896162007533298e-10,1.8796641617702957e-17,2.2351741790771488e-09 --3.2,1000,-70.97546172407125,False,True,True,0.0,0.07459460277496091,0.4512611730146571,0.4512610321311455,99,0.005090916156764109,-5.066387987273229e-18,3.141592653589793,-6.308059133449495e-10,1.9895805048376014e-17,-7.450580596923829e-10 --3.0,1000,-58.80314310968942,False,True,True,0.0,0.07923938940553231,0.4512609855578532,0.45126105230688707,100,0.0050909124314895615,-2.23517416722443e-09,3.141592653589793,1.350867035527277e-10,3.9099955163527365e-19,2.2351741790771488e-09 --2.8,1000,-48.360051776730394,False,True,True,0.0,0.09538863576343395,0.45126073950877016,0.4512610409750512,67,0.005090907216088642,-3.725290281983783e-09,3.141592653589793,6.834681597605438e-10,4.834157176837103e-18,-9.685754776000977e-09 --2.6,1000,-39.4471787485843,False,True,True,0.0,0.08749353621430453,0.45126097810662513,0.4512611060985756,81,0.005090912431474936,-2.9802322420823856e-09,3.141592653589793,6.801226267169552e-10,7.299997668614735e-18,-1.2665987014770509e-08 --2.4,1000,-31.625950602597886,False,True,True,0.0,0.05823923296709817,0.451261070728794,0.45126102126293943,81,0.005090913176451042,2.980232151805638e-09,3.141592653589793,-4.3769940620243564e-11,3.2322363822523224e-18,2.2351741790771488e-09 --2.2,1000,-24.9426515565825,False,True,True,0.0,0.05391345280571466,0.45126106650151987,0.45126108707586,80,0.005090913921610069,-7.450580441772433e-10,3.141592653589793,4.502221829433901e-10,7.058139855639675e-18,2.2351741790771488e-09 --2.0,1000,-19.422363270351205,False,True,True,0.0,0.05080219791387097,0.45126135302203085,0.45126104827980185,82,0.005090919882065934,1.4901161266096377e-09,3.141592653589793,-8.18852514352011e-10,2.24343416773434e-17,-7.450580596923829e-10 --1.8,1000,-14.542520000738147,False,True,True,0.0,0.024093682547462768,0.4512610632815351,0.4512610443809036,70,0.0050909131765259925,2.235174167982266e-09,3.141592653589793,-2.3435907964227197e-09,2.2473555674609903e-16,1.1175870895385742e-08 --1.6,1000,-10.397129576179713,False,True,True,0.0,0.02261333743670461,0.45126086837307156,0.45126109397670255,89,0.005090909451228242,-7.450580784077692e-10,3.141592653589793,2.114995474335571e-10,9.38364234116189e-19,2.2351741790771488e-09 --1.4,1000,-7.156314162853417,False,True,True,0.0,0.0627308728383455,0.4512609237471231,0.45126103622295444,55,0.005090910196312077,1.4901161272736662e-09,3.141592653589793,-9.532294651738329e-10,3.233831771313515e-17,2.2351741790771488e-09 --1.2,1000,-4.891710033330995,False,True,True,0.0,0.07301120193723598,0.4512609557563846,0.4512610007947114,55,0.0050909124315087085,-5.215406387033862e-09,3.141592653589793,4.2125964921375934e-10,5.028138427594487e-19,-1.2665987014770509e-08 --1.0,1000,-3.1485971992380732,False,True,True,0.0,0.049880472930644196,0.4512611027404466,0.4512610922294897,56,0.00509091541170237,-3.725290306710961e-09,3.141592653589793,1.4668866123088076e-09,5.98809258197507e-17,-1.564621925354004e-08 --0.8,1000,-1.910726706045262,False,True,True,0.0,0.06840939819812793,0.4512608832743373,0.45126103982210225,69,0.005090909451230599,7.450580443330869e-10,3.141592653589793,4.5543762887202847e-10,1.404200212291121e-17,2.2351741790771488e-09 --0.6,1000,-1.2098444188882098,False,True,True,0.0,0.06812135957605926,0.4512610366986629,0.45126102750851094,48,0.005090913921597946,-3.7252902952010424e-09,3.141592653589793,1.3916286373954355e-09,5.19282005407083e-17,-1.2665987014770509e-08 --0.4,1000,-0.8439303434510845,False,True,True,0.0,0.11575779982628225,0.451261040930864,0.45126110094466393,43,0.005090913176550169,1.373285418396659e-17,3.141592653589793,-6.472562430171036e-10,2.0947032116405946e-17,2.2351741790771488e-09 --0.2,1000,-0.6302173427289368,False,True,True,0.0,0.09416441931638662,0.45126110798480873,0.4512610883953983,55,0.005090913176521058,6.705522521703281e-09,3.141592653589793,-1.9147511792460395e-09,7.740154850570866e-17,1.1175870895385742e-08 -0.2,1000,-0.6262944127261929,False,True,True,0.0,0.06531727555844687,0.45126079708722744,0.45126106564392027,53,0.005090910196310887,-1.11758708887617e-08,3.141592653589793,2.2369845887428077e-09,6.265253813890842e-17,-1.862645149230957e-08 -0.4,1000,-0.8248085885803093,False,True,True,0.0,0.08091793179022744,0.45126097810722704,0.45126105795264265,25,0.005090912431488515,-2.9802322286822103e-09,3.141592653589793,7.444098776599193e-10,9.963052279464774e-18,-9.685754776000977e-09 -0.6,1000,-1.1562586219221422,False,True,True,0.0,0.06020923299600085,0.45126112509230837,0.4512610905463655,38,0.005090915411705135,-1.4901161248542845e-09,3.141592653589793,8.924409189569718e-10,2.763435668414174e-17,-6.7055225372314455e-09 -0.8,1000,-2.888872958782562,False,True,True,0.0,0.20708435693654748,0.4512611144237963,0.4512610565676187,264,0.0050909146666672945,7.450580694764387e-10,3.141592653589793,6.114335009888207e-11,9.200346898066076e-19,2.2351741790771488e-09 -1.0,1000,-3.0848904265708157,False,True,True,0.0,0.08428711337128927,0.45126072460653155,0.45126107608134547,72,0.005090907216064272,-5.2154064258526114e-09,3.141592653589793,1.1121050852916702e-09,1.7438318047562394e-17,-7.450580596923829e-10 -1.2,1000,-9.015936854684051,False,True,True,0.0,0.4623873233795167,2.0694568510390474,1.4219926414082278,835,0.04911105036733082,0.04997572898864744,3.1415623274788573,-0.010396070831102128,0.0014571889962185663,0.1 -1.4,1000,-7.052976776729152,False,True,True,0.0,0.08887259759164043,0.4512610665011574,0.45126106227746055,36,0.005090913921601892,-7.450580524240417e-10,3.141592653589793,5.931956078762524e-10,1.3451955567007996e-17,-9.685754776000977e-09 -1.6,1000,-10.24184296269372,False,True,True,0.0,0.02039596205507216,0.4512610228099168,0.45126110447952655,86,0.005090912431470641,1.4901161114342618e-09,3.141592653589793,-6.481910784800014e-10,1.2459007031533167e-17,2.2351741790771488e-09 -1.8,1000,-14.355906282941111,False,True,True,0.0,0.06077646792754172,0.4512611762332905,0.4512610302140647,78,0.005090916901817853,-2.9802322476177013e-09,3.141592653589793,5.080364755164609e-10,2.205278274629901e-18,-6.7055225372314455e-09 -2.0,1000,-19.373339030740922,False,True,True,0.0,0.06788837785525115,0.4512612986617705,0.45126105825020707,79,0.005090917646884132,5.960464476605711e-09,3.141592653589793,-1.424306510001635e-09,3.4300736540040964e-17,1.1175870895385742e-08 -2.2,1000,-25.160403655604807,False,True,True,0.0,0.07037172303826232,0.45126098132698345,0.451261044745054,77,0.005090913176567232,-5.960464446410987e-09,3.141592653589793,9.076880033613848e-10,4.856023055301379e-18,-1.2665987014770509e-08 -2.4,1000,-32.52914325379317,False,True,True,0.0,0.08312715070965496,0.4512610004579085,0.45126109793942504,91,0.005090912431464636,-7.45058073725673e-10,3.141592653589793,6.80296300567423e-10,1.8349106084256225e-17,2.2351741790771488e-09 -2.6,1000,-40.70714085613804,False,True,True,0.0,0.06276710046347918,0.45126110595790175,0.4512610757819747,84,0.005090916156729371,-6.705522577267946e-09,3.141592653589793,2.118214827927359e-09,1.0478634585746964e-16,-1.564621925354004e-08 -2.8,1000,-50.022545773998196,False,True,True,0.0,0.02972443401813562,0.4512610163702922,0.451261048704106,91,0.005090910941310316,7.450580542714065e-09,3.141592653589793,-1.2674531146942533e-09,1.3644829955445762e-17,1.4156103134155274e-08 -3.0,1000,-60.419387817006665,False,True,True,0.0,0.03022841364145302,0.4512612986608017,0.451261132812724,104,0.005090917646862278,5.960464454317603e-09,3.141592653589793,-1.1409397796780578e-09,1.4845437284487008e-17,2.2351741790771488e-09 -3.2,1000,-72.53850761619567,False,True,True,0.0,0.08791580401595252,0.45126085770304647,0.4512610859866845,97,0.005090908706156692,1.4901160869593366e-09,3.141592653589793,-6.918233901669666e-10,1.4732231505263272e-17,2.2351741790771488e-09 -3.4,1000,-86.58176232953632,False,True,True,0.0,0.11059764151310814,0.45126105058584765,0.45126110021842436,216,0.005090915411689976,-8.940696737632307e-09,3.141592653589793,1.146296783906366e-09,3.180925754524105e-18,-1.862645149230957e-08 -3.6,1000,-100.47168581714693,False,True,True,0.0,0.026494041119443406,0.45126103771160614,0.45126110592962243,117,0.005090912431482521,2.9802322432167646e-09,3.141592653589793,-1.1799088999900496e-10,1.6205820701970143e-18,5.21540641784668e-09 -3.8,1000,-116.0042592357965,False,True,True,0.0,0.06009005592308492,0.45126096320550413,0.4512610999211553,104,0.005090912431475789,-4.47034836050332e-09,3.141592653589793,7.69376362311808e-10,5.1952029773723384e-18,-3.725290298461914e-09 -4.0,1000,-132.4339012667871,False,True,True,0.0,0.0806245483017465,0.4512611879146213,0.45126110285964666,104,0.005090916156737096,1.490116086787125e-09,3.141592653589793,-1.405959228875064e-09,7.899586599387368e-17,2.2351741790771488e-09 -4.2,1000,-151.1630464373494,False,True,True,0.0,0.10855682064493402,0.45126113576210414,0.45126109559646116,117,0.005090916156771934,-3.725290294191705e-09,3.141592653589793,-7.056632023241865e-10,5.812492442886797e-17,-9.685754776000977e-09 -4.4,1000,-171.73057988969813,False,True,True,0.0,0.04384944459275776,0.4512610771702058,0.45126105560851854,110,0.0050909146666517436,-2.9802322395995732e-09,3.141592653589793,7.329992161318269e-10,9.46020568737822e-18,-6.7055225372314455e-09 -4.6,1000,-193.27278066897688,False,True,True,0.0,0.07176362056990106,0.4512610995237483,0.45126110088138455,97,0.005090914666692406,-7.450580184760837e-10,3.141592653589793,1.1583170529190096e-09,5.873234139444755e-17,-3.725290298461914e-09 -4.8,1000,-215.63536554790676,False,True,True,0.0,0.04135360916578222,0.4512613711419618,0.45126106485472783,117,0.005090920627123083,5.915768059515649e-18,3.141592653589793,1.0307360527157374e-09,5.3120840580982434e-17,-7.450580596923829e-10 -5.0,1000,-238.06508497370996,False,True,True,0.0,0.07871416769665823,0.4512611571013819,0.45126105818642265,116,0.005090917646898472,-8.195638643070074e-09,3.141592653589793,1.755790681004112e-09,4.3826032611748305e-17,-1.862645149230957e-08 -5.2,1000,-261.5512016011545,False,True,True,0.0,0.04049543479130847,0.4512610590491435,0.45126104474269274,94,0.005090913921569554,-1.490116144833024e-09,3.141592653589793,2.5703300032127226e-10,5.834309896740284e-19,-3.725290298461914e-09 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+3000960.0,1000.0,-0.0032582817000000003,0.021450992673635483,0.986194908618927,9.162201575025896e-05,3.3002601181040516,0.23382339477539063,-0.016677700311231548,0.0,0.7573037473222102,0.014564358745556092,0.009489068825714639,0.0001338764823695013,0.0019681885583906285,0.0011195043367590784 diff --git a/models/hybrid_cv01_s5775_meta.json b/models/hybrid_cv01_s5775_meta.json index 8e46e9a..4ce7a1a 100644 --- a/models/hybrid_cv01_s5775_meta.json +++ b/models/hybrid_cv01_s5775_meta.json @@ -18,7 +18,7 @@ "n_envs": 32, "gamma": 0.99, "save_path": "models/hybrid_cv01_s5775.zip", - "seed": null, + "seed": 5775, "ent_coef": 0.0, "learning_rate": 0.0003, "clip_range": 0.2, @@ -28,7 +28,9 @@ "start_speed": 1.0, "continuous_actions": true, "reward_limit": 50.0, - "max_episode_steps": 1000 + "log_std_init": -3.2188758248682006, + "max_episode_steps": 1000, + "acc": 0.1 }, "config_source": "experiments/hybrid_cv01_s5775.yaml" } \ No newline at end of file diff --git a/models/hybrid_cv01_s5775_play_results.csv b/models/hybrid_cv01_s5775_play_results.csv index 5f89f2a..bb3c22d 100644 --- a/models/hybrid_cv01_s5775_play_results.csv +++ b/models/hybrid_cv01_s5775_play_results.csv @@ -1,101 +1,101 @@ start_speed,ep_steps,ep_reward,terminated,truncated,no_crash,t_min_start,t_min_min,t_min_final,t_min_mean_last100,t_min_settle_step,x_pos_final,x_vel_final,theta_final,theta_dot_final,energy_final,acc_final --10.0,1000,-1216.006228642393,False,True,True,0.0,0.07097375415806041,0.5272690747722691,0.5272691049037035,160,0.006950317323126212,-1.4901162058456526e-09,3.141592653589793,-7.164325339886933e-10,3.744967923363096e-17,-2.9802322387695314e-09 --9.8,1000,-1175.0660856237891,False,True,True,0.0,0.1028561087041568,0.527269062958598,0.527269050647177,167,0.006950315833138785,2.9802322901075002e-09,3.141592653589793,-6.309112685040433e-11,2.7596546312970865e-18,8.940696716308595e-09 --9.6,1000,-1110.8256280222122,False,True,True,0.0,0.07017481003940916,0.5272693306652664,0.5272691566700497,160,0.006950323283658685,1.4901161040940711e-09,3.141592653589793,9.824471689699315e-10,6.400994847397621e-17,0.0 --9.4,1000,-1060.4379451015707,False,True,True,0.0,0.05674469345739176,0.5272690182548933,0.5272691390577138,160,0.006950315833132988,-1.4901160754918356e-09,3.141592653589793,5.836658966087603e-10,9.446217549121063e-18,-2.9802322387695314e-09 --9.2,1000,-1013.1995683610857,False,True,True,0.0,0.36472952612395765,0.5272690449733687,0.5272691405466486,128,0.00695031732321631,-4.470348351227306e-09,3.141592653589793,-9.472297317852382e-10,9.719868412416866e-17,-2.9802322387695314e-09 --9.0,1000,-972.5319101993964,False,True,True,0.0,0.26610876462790284,0.5272690182545313,0.5272691153797665,133,0.006950315833123451,-1.4901160845692114e-09,3.141592653589793,3.532904993723784e-10,2.0864932641438487e-18,-2.9802322387695314e-09 --8.8,1000,-911.315454983043,False,True,True,0.0,0.03628127860186514,0.5272692623251881,0.5272691557764158,161,0.006950320303438759,5.960464475681981e-09,3.141592653589793,-2.0071527611685646e-09,9.956105141975783e-17,8.940696716308595e-09 --8.6,1000,-849.8975915217934,False,True,True,0.0,0.04061706364154819,0.5272692443412883,0.527269112285172,147,0.006950321793552025,-1.4901161237599155e-09,3.141592653589793,1.0042601872263164e-09,3.657250624247073e-17,-2.9802322387695314e-09 --8.4,1000,-808.392252229777,False,True,True,0.0,0.09910584974591125,0.5272693306665943,0.527269106397518,138,0.0069503232836936844,1.4901161421845513e-09,3.141592653589793,5.418093096758448e-10,2.386167744638799e-17,2.9802322387695314e-09 --8.2,1000,-770.9203747661155,False,True,True,0.0,0.06261433739059416,0.5272689468290986,0.5272691495888913,139,0.006950314342948613,-2.980232268271072e-09,3.141592653589793,-7.305234032258856e-10,5.289540851133702e-17,-2.9802322387695314e-09 --8.0,1000,-733.5488788653129,False,True,True,0.0,0.13525436351971054,0.5272689409221445,0.5272691566409274,154,0.006950313597951974,-7.450580224088651e-10,3.141592653589793,4.821963707454553e-10,8.310579983139305e-18,-2.9802322387695314e-09 --7.8,1000,-694.2512860764255,False,True,True,0.0,0.12320950564451409,0.5272691611010386,0.5272690998517295,153,0.006950318813358673,1.490116156608691e-09,3.141592653589793,-7.188608766539743e-10,1.6236409014282593e-17,2.9802322387695314e-09 --7.6,1000,-646.0091236510435,False,True,True,0.0,0.11865121778571777,0.5272688188850303,0.5272691046087781,142,0.006950311362747972,-4.470348353236106e-09,3.141592653589793,5.107767977088172e-10,2.0315188817584503e-19,-2.9802322387695314e-09 --7.4,1000,-615.3708753331417,False,True,True,0.0,0.13338578067960496,0.5272693039452333,0.527269118532761,148,0.006950321793533494,4.470348334939655e-09,3.141592653589793,-1.5585404705787259e-09,6.177223906775696e-17,8.940696716308595e-09 --7.2,1000,-578.0721326176887,False,True,True,0.0,0.10052256731921361,0.5272690896771909,0.5272691263856444,154,0.0069503173232253355,1.9379276726398488e-17,3.141592653589793,3.6726658293529247e-10,6.7442372171299586e-18,2.9802322387695314e-09 --7.0,1000,-549.6530437550332,False,True,True,0.0,0.025626465644594067,0.5272692443425037,0.5272691699387956,149,0.0069503217935840585,-1.4901160897993264e-09,3.141592653589793,-1.8689773706396757e-09,2.03613976088228e-16,2.9802322387695314e-09 --6.8,1000,-515.149445074285,False,True,True,0.0,0.07442785898684415,0.5272690331544774,0.5272690673657179,140,0.006950315833091435,1.4018974303487458e-19,3.141592653589793,-9.575672399239467e-10,4.584675094877827e-17,2.9802322387695314e-09 --6.6,1000,-482.87144997209634,False,True,True,0.0,0.07015121130624775,0.5272690331551838,0.5272690843229948,140,0.006950315833110052,1.8934988093228906e-17,3.141592653589793,4.387702716115432e-10,9.625967646229329e-18,2.9802322387695314e-09 --6.4,1000,-447.5781485168516,False,True,True,0.0,0.0862686592963971,0.5272691045792534,0.5272690619513702,126,0.006950317323249079,1.4901161620006902e-09,3.141592653589793,8.5042841682923e-10,4.9944018985130225e-17,5.960464477539063e-09 --6.2,1000,-413.82368241298343,False,True,True,0.0,0.15122981574381264,0.5272691610982797,0.5272690543334546,149,0.006950318813285958,1.4901160813540133e-09,3.141592653589793,-5.553318347255356e-10,8.254806325239415e-18,-2.9802322387695314e-09 --6.0,1000,-377.22043402320776,False,True,True,0.0,0.09333825683345458,0.5272690778587197,0.5272691060377832,142,0.006950315833111334,4.4703483780646184e-09,3.141592653589793,-2.3626319362333174e-09,1.834756121776454e-16,8.940696716308595e-09 --5.8,1000,-346.6915879954291,False,True,True,0.0,0.15694329144446426,0.5272695418531439,0.527269162086858,135,0.006950329244150603,1.5408515309845886e-17,3.141592653589793,4.2683104239706885e-10,9.109237004327464e-18,-1.4901161193847656e-08 --5.6,1000,-320.31220166847953,False,True,True,0.0,0.0586136281987509,0.5272693306653138,0.52726914596172,136,0.006950323283659934,1.4901161076901197e-09,3.141592653589793,2.488757317829059e-10,7.915716868437835e-18,-2.9802322387695314e-09 --5.4,1000,-294.60709119200914,False,True,True,0.0,0.16239304019307915,0.5272691729180702,0.5272691733289296,127,0.006950320303434917,-2.980232243732701e-09,3.141592653589793,-5.687145686112961e-10,3.756172009298023e-17,-2.9802322387695314e-09 --5.2,1000,-267.1104899055143,False,True,True,0.0,0.05395154654979691,0.5272693871883278,0.5272691485215792,127,0.006950324773802554,1.4901161340957448e-09,3.141592653589793,-1.265610549828406e-09,6.233965924407572e-17,-2.9802322387695314e-09 --5.0,1000,-241.816011027452,False,True,True,0.0,0.08176604853819174,0.5272690480555325,0.5272691371549112,77,0.006950315833088627,1.4901161173566756e-09,3.141592653589793,-4.619182739669773e-10,4.895528963604599e-18,2.9802322387695314e-09 --4.8,1000,-219.39939532665747,False,True,True,0.0,0.285707738268562,0.527268818883739,0.5272691379863744,77,0.006950311362713937,-4.470348388620187e-09,3.141592653589793,2.2516147828176774e-10,2.461389406133682e-18,-2.9802322387695314e-09 --4.6,1000,-197.76875809721824,False,True,True,0.0,0.1712197272633388,0.5272688754055797,0.5272691504210197,120,0.006950312852824353,-4.470348394393201e-09,3.141592653589793,-5.047411474391466e-10,4.529387646008402e-17,-2.9802322387695314e-09 --4.4,1000,-175.54684549886372,False,True,True,0.0,0.1787693649095524,0.5272689319295867,0.5272690995530428,128,0.006950314342992026,-4.470348341030473e-09,3.141592653589793,1.3281605320551643e-09,3.8819124783077314e-17,-8.940696716308595e-09 --4.2,1000,-154.47436416108184,False,True,True,0.0,0.08416675586841287,0.5272689319289169,0.5272691479249774,122,0.006950314342974373,-4.470348359192021e-09,3.141592653589793,-2.691131577174125e-10,2.5643397437025065e-17,2.9802322387695314e-09 --4.0,1000,-136.26539720025977,False,True,True,0.0,0.06399156373093057,0.5272692058052328,0.5272690580260957,122,0.006950318813377238,5.960464533232308e-09,3.141592653589793,-1.7798193803315177e-09,7.00659171339369e-17,8.940696716308595e-09 --3.8,1000,-118.51385634371616,False,True,True,0.0,0.03138211369514487,0.5272691760001134,0.5272690825951198,100,0.006950318813303647,2.980232217793462e-09,3.141592653589793,-8.715749504281502e-10,1.6448079269892437e-17,5.960464477539063e-09 --3.6,1000,-103.24215865398509,False,True,True,0.0,0.3319691037833351,0.5272690182520959,0.527269103240632,88,0.0069503158330592625,-1.4901161513964052e-09,3.141592653589793,9.268731135026549e-10,3.025342553384835e-17,-2.9802322387695314e-09 --3.4,1000,-87.43235993525765,False,True,True,0.0,0.054521836240689146,0.5272690331560473,0.5272690421994862,108,0.006950315833132812,4.2283383455483466e-17,3.141592653589793,1.0240198863621414e-09,5.2430836818472526e-17,-2.9802322387695314e-09 --3.2,1000,-73.76983386093582,False,True,True,0.0,0.10180810492879808,0.5272693306647863,0.5272691377501143,115,0.006950323283646031,1.4901160938827374e-09,3.141592653589793,2.3641008676324495e-09,3.1578671610790184e-16,-2.9802322387695314e-09 --3.0,1000,-60.32871307305607,False,True,True,0.0,0.1458270448837823,0.5272693039478201,0.5272691063983814,109,0.006950321793601674,4.4703484050175625e-09,3.141592653589793,-1.095244819151426e-09,2.1008808822341534e-17,5.960464477539063e-09 --2.8,1000,-48.874289013729594,False,True,True,0.0,0.07467967950620065,0.5272695063960608,0.5272691007435492,105,0.0069503247737606405,1.3411045046000805e-08,3.141592653589793,-2.119545102425295e-09,3.0298488215675224e-17,1.788139343261719e-08 --2.6,1000,-39.67947451322571,False,True,True,0.0,0.09960768594295993,0.5272689825425538,0.5272691043422093,95,0.006950315088055488,-2.2351741572249254e-09,3.141592653589793,1.5998954129717122e-09,9.472081957043674e-17,-2.9802322387695314e-09 --2.4,1000,-31.838831451316835,False,True,True,0.0,0.16168362599224834,0.5272691536471241,0.5272690769725584,80,0.00695031881327081,7.450580057500415e-10,3.141592653589793,-2.786660857400816e-10,2.0840711019497197e-18,-2.9802322387695314e-09 --2.2,1000,-24.841865897562318,False,True,True,0.0,0.12407854407564131,0.5272692741442305,0.5272691193664301,83,0.006950321793568361,1.4901161322450465e-09,3.141592653589793,1.7803500597627574e-09,1.8612182325581337e-16,-2.9802322387695314e-09 --2.0,1000,-19.071575979068836,False,True,True,0.0,0.14452295005321505,0.5272691610995492,0.5272691088938387,89,0.006950318813319419,1.4901161146007174e-09,3.141592653589793,5.369870514777814e-10,2.3529708278314138e-17,5.960464477539063e-09 --1.8,1000,-14.316057283501758,False,True,True,0.0,0.16935576781477774,0.527269247424614,0.5272690730184363,69,0.00695032030345431,4.4703483726882935e-09,3.141592653589793,-2.2072695203527833e-09,1.5492130697592013e-16,8.940696716308595e-09 --1.6,1000,-10.423051654472003,False,True,True,0.0,0.15483869567104458,0.5272691878193977,0.5272691125833243,84,0.006950320303439336,-1.4901161201166795e-09,3.141592653589793,2.286985964253874e-09,2.285467165467958e-16,-2.9802322387695314e-09 --1.4,1000,-7.283369286685086,False,True,True,0.0,0.04319035460053623,0.5272687058403955,0.5272691285839213,76,0.00695030838250249,-4.470348366811748e-09,3.141592653589793,-2.1736963519035835e-10,2.2071665111099395e-17,-2.9802322387695314e-09 --1.2,1000,-4.94179873339676,False,True,True,0.0,0.06665119002401976,0.5272695212982796,0.527269050707132,76,0.006950324773788306,1.4901161193730875e-08,3.141592653589793,-1.9800371667919733e-09,1.200113163462877e-17,1.788139343261719e-08 --1.0,1000,-3.2902780028741807,False,True,True,0.0,0.09090670933886626,0.5272691194789267,0.5272690856892187,70,0.006950317323209831,2.980232240902602e-09,3.141592653589793,-1.441735026750033e-09,6.540383437973527e-17,2.9802322387695314e-09 --0.8,1000,-2.123588513635873,False,True,True,0.0,0.14133394978103475,0.5272690896771545,0.5272691408449655,70,0.006950317323224377,1.6601151761650355e-17,3.141592653589793,1.006557191843562e-09,5.0657869188479215e-17,-2.9802322387695314e-09 --0.6,1000,-1.3684259031069392,False,True,True,0.0,0.3827977694880887,0.5272690331545445,0.5272690752788664,34,0.006950315833093207,1.3088776617447076e-18,3.141592653589793,-4.518846888535986e-10,1.0209988593176732e-17,-2.9802322387695314e-09 --0.4,1000,-1.0239594870172728,False,True,True,0.0,0.1832310978860211,0.5272691163962228,0.5272690617135042,64,0.006950318813323525,-2.980232239223633e-09,3.141592653589793,7.293420241189765e-10,9.301795371362701e-18,-2.9802322387695314e-09 --0.2,1000,-0.7986616005795687,False,True,True,0.0,0.12397191043762144,0.5272690033520695,0.5272690707569567,57,0.006950315833089138,-2.980232241453606e-09,3.141592653589793,2.3694414150611347e-09,2.1453866608094395e-16,-8.940696716308595e-09 -0.2,1000,-0.7886599736699934,False,True,True,0.0,0.1483580562041341,0.527269003352314,0.5272691063352319,54,0.0069503158330955815,-2.9802322348848547e-09,3.141592653589793,-6.639627512287697e-11,6.640078547392349e-18,-2.9802322387695314e-09 -0.4,1000,-1.0074720362890355,False,True,True,0.0,0.13319727778434784,0.5272694555270909,0.5272690843211703,67,0.006950327753988575,-2.980232271266446e-09,3.141592653589793,2.2603463679660457e-09,1.9253560545822472e-16,-2.086162567138672e-08 -0.6,1000,-1.3650324485553456,False,True,True,0.0,0.05453643367476027,0.5272693306653328,0.5272690823590357,67,0.006950323283660436,1.490116107191602e-09,3.141592653589793,-1.5371381816286724e-09,9.634476884737065e-17,2.9802322387695314e-09 -0.8,1000,-2.1292785879344702,False,True,True,0.0,0.13179010098533087,0.5272690629559398,0.5272690741475425,64,0.0069503158330687246,2.9802322156690437e-09,3.141592653589793,-4.940200417536935e-10,1.920737676450446e-18,5.960464477539063e-09 -1.0,1000,-3.3967339223104274,False,True,True,0.0,0.11144360899925213,0.5272691163969578,0.5272690659379439,71,0.0069503188133429,-2.9802322193381804e-09,3.141592653589793,1.3035518779077467e-09,5.055439390270079e-17,-8.940696716308595e-09 -1.2,1000,-5.012626768867913,False,True,True,0.0,0.10790394959709426,0.5272693306665336,0.527269158695749,73,0.006950323283692084,1.4901161393021362e-09,3.141592653589793,1.0257427596652454e-10,3.1647729989641994e-18,2.9802322387695314e-09 -1.4,1000,-7.569144021438425,False,True,True,0.0,0.12175419018637265,0.5272690629557493,0.5272690769426316,73,0.006950315833063701,2.980232211013413e-09,3.141592653589793,-2.2561980713990416e-09,1.9172243721351354e-16,8.940696716308595e-09 -1.6,1000,-10.74520608011957,False,True,True,0.0,0.09152405265085008,0.5272690331536848,0.5272690681973721,87,0.006950315833070545,-2.1425269921240225e-17,3.141592653589793,-1.953229280611733e-09,1.9075523155549052e-16,2.9802322387695314e-09 -1.8,1000,-14.904418393384828,False,True,True,0.0,0.09490061662926996,0.5272690331535074,0.527269048979718,86,0.006950315833065868,-2.6356296211918898e-17,3.141592653589793,4.5778657873896217e-10,1.0478427464556231e-17,2.9802322387695314e-09 -2.0,1000,-19.996829502503942,False,True,True,0.0,0.07877460846873928,0.5272690331526353,0.5272690659354999,80,0.006950315833042885,-4.9679029950030225e-17,3.141592653589793,-6.839274546463599e-10,2.338783849908356e-17,2.9802322387695314e-09 -2.2,1000,-26.000421498988917,False,True,True,0.0,0.06968598184954582,0.5272688188839872,0.5272691227564804,85,0.006950311362720482,-4.470348381818579e-09,3.141592653589793,4.3607671605524863e-10,6.0040219811709765e-21,-8.940696716308595e-09 -2.4,1000,-32.754000653539336,False,True,True,0.0,0.11653261247545822,0.5272689617297419,0.5272691354270767,62,0.006950314342934998,-1.4901161607381124e-09,3.141592653589793,-6.256775274159975e-10,3.000716345153019e-17,2.9802322387695314e-09 -2.6,1000,-40.62605532624265,False,True,True,0.0,0.15584598047609427,0.5272693604676744,0.5272690605826625,99,0.006950323283660852,4.470348345660538e-09,3.141592653589793,-5.416485590606896e-11,7.7173410073111e-18,5.960464477539063e-09 -2.8,1000,-49.598249408081585,False,True,True,0.0,0.09813922593011816,0.5272693039467654,0.5272691012795822,98,0.006950321793573874,4.4703483766409215e-09,3.141592653589793,-3.744449182341228e-10,2.634648204487011e-19,5.960464477539063e-09 -3.0,1000,-59.657528155816706,False,True,True,0.0,0.11432963142747013,0.5272692027208817,0.5272691137140767,106,0.0069503203034478565,7.914806553680966e-18,3.141592653589793,1.034020542459163e-09,5.345992419209536e-17,-2.9802322387695314e-09 -3.2,1000,-71.12195606361614,False,True,True,0.0,0.08699937526612289,0.527269238434154,0.5272691018460455,99,0.006950321048550204,7.45058113298623e-10,3.141592653589793,6.083791808969262e-10,2.3316595630406614e-17,-2.9802322387695314e-09 -3.4,1000,-83.9275711949165,False,True,True,0.0,0.02920963674824919,0.5272693306668236,0.5272691586960235,105,0.006950323283699728,1.490116147575504e-09,3.141592653589793,1.0250952810261284e-10,3.163144265498565e-18,2.9802322387695314e-09 -3.6,1000,-98.96783418889146,False,True,True,0.0,0.09465154643977958,0.5272689884519927,0.5272691410816749,111,0.006950315833117662,-4.470348331605443e-09,3.141592653589793,9.519354986014871e-10,1.2746234096345323e-17,-8.940696716308595e-09 -3.8,1000,-115.35710336068281,False,True,True,0.0,0.11027909573295624,0.5272688754079404,0.5272690987210094,118,0.006950312852886575,-4.470348331420053e-09,3.141592653589793,-9.398044210460351e-11,1.4634876400736682e-17,-2.9802322387695314e-09 -4.0,1000,-132.9477908909668,False,True,True,0.0,0.1074842655664521,0.5272690033526659,0.5272691165095387,119,0.006950315833104854,-2.980232224175162e-09,3.141592653589793,1.2174358319083176e-09,4.226597732432561e-17,-2.9802322387695314e-09 -4.2,1000,-151.84618111876492,False,True,True,0.0,0.02966204285621678,0.527269175999667,0.5272690846192095,132,0.0069503188132918866,2.9802322044298893e-09,3.141592653589793,4.1289175096080595e-10,2.5270004827013645e-17,2.9802322387695314e-09 -4.4,1000,-172.40980835098887,False,True,True,0.0,0.12914766135231087,0.527269003351199,0.5272690667687607,140,0.006950315833066194,-2.9802322658487314e-09,3.141592653589793,-3.7469498637805695e-10,2.262748970248409e-17,-2.9802322387695314e-09 -4.6,1000,-194.99348063329992,False,True,True,0.0,0.16928640182769797,0.5272692443427743,0.5272691250194559,125,0.0069503217935911925,-1.4901160833624699e-09,3.141592653589793,1.2739578419349464e-09,6.327520142311793e-17,-2.9802322387695314e-09 -4.8,1000,-218.21348931254136,False,True,True,0.0,0.15699428294082649,0.5272687890823967,0.5272690924716545,124,0.0069503113627395925,-7.450580601720185e-09,3.141592653589793,4.208164236132526e-10,5.256631945237644e-18,-8.940696716308595e-09 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+9.8,1000,-1630.1775545881028,False,True,True,0.0,0.17311442174054717,0.51198587303348,0.5119859692961439,243,0.006553239189088345,-3.259629011154175e-09,3.141592653589793,6.777132277309352e-10,6.186414613706166e-18,-6.51925802230835e-09 +10.0,1000,-1702.1787153859564,False,True,True,0.0,0.09038293605605446,0.5119858912238745,0.5119859874865362,243,0.006553239654749632,-3.259629011154175e-09,3.141592653589793,6.791054556792051e-10,6.235483192191886e-18,-6.51925802230835e-09 diff --git a/models/hybrid_cv01_s5775_vecnorm.pkl b/models/hybrid_cv01_s5775_vecnorm.pkl new file mode 100644 index 0000000..b7b1913 Binary files /dev/null and b/models/hybrid_cv01_s5775_vecnorm.pkl differ diff --git a/scripts/play_ppo.py b/scripts/play_ppo.py index 8a701a3..4e9a589 100644 --- a/scripts/play_ppo.py +++ b/scripts/play_ppo.py @@ -135,8 +135,13 @@ def main() -> None: _ = parser.add_argument( "--randomize-start", action=argparse.BooleanOptionalAction, - default=config.training.randomize_start, - help="Randomise initial pendulum speed each episode (default from model sidecar).", + default=None, + help=( + "Randomise initial pendulum speed each episode. Default: the model " + "sidecar's value for single-speed playback, but forced OFF for " + "--speed-sweep so each row uses its exact requested speed. Pass " + "--randomize-start to re-enable it for a sweep." + ), ) _ = parser.add_argument( "--start-speed", @@ -165,8 +170,14 @@ def main() -> None: "--save-png", "--no-save-png", action=argparse.BooleanOptionalAction, - default=True, - help="Save 7-panel trajectory plot per episode alongside the model (default True).", + default=None, + help=( + "Save the per-episode trajectory plot alongside the model. Default: on " + "for single-speed playback, off for --speed-sweep (which would otherwise " + "write one PNG per swept speed). Recording traces needs render_mode " + "'plot'; --render-mode none is promoted to 'plot' (file only, no window) " + "when this is on, but --render-mode play-back cannot also save." + ), ) _ = parser.add_argument( "--save-csv", @@ -183,6 +194,31 @@ def main() -> None: ) args = parser.parse_args() + if args.save_png is None: + args.save_png = not args.speed_sweep + + if args.randomize_start is None: + args.randomize_start = False if args.speed_sweep else config.training.randomize_start + if args.speed_sweep and config.training.randomize_start: + LOGGER.info( + "--speed-sweep: randomize_start forced off (sidecar has it on) so each " + "swept speed is exact; pass --randomize-start to override." + ) + + # Trajectory traces are only recorded while render_mode == "plot" (see + # AntiPendulumEnv.step). Make --save-png actually take effect instead of + # silently doing nothing when the user asked not to render to a window. + render_mode = args.render_mode + if args.save_png: + if render_mode == "none": + render_mode = "plot" + LOGGER.info("--save-png: rendering to file (render_mode 'plot', no window).") + elif render_mode != "plot": + LOGGER.warning( + "--save-png has no effect with --render-mode %s; use 'plot' (or 'none') to save PNGs.", + render_mode, + ) + mep = args.max_episode_steps if args.max_episode_steps is not None else config.training.max_episode_steps speeds = SWEEP_SPEEDS if args.speed_sweep else [args.start_speed] @@ -194,11 +230,12 @@ def main() -> None: "conf": AntiPendulumConfig( start_speed=speeds[0], randomize_start=args.randomize_start, - render_mode=args.render_mode, + render_mode=render_mode, reward_fac=config.reward, rail_limit=config.training.rail_limit, reward_limit=config.training.reward_limit, continuous_actions=args.continuous_actions, + acc=config.training.acc, ), }, max_episode_steps=mep, diff --git a/scripts/train_ppo.py b/scripts/train_ppo.py index 9b419c1..506a677 100644 --- a/scripts/train_ppo.py +++ b/scripts/train_ppo.py @@ -15,6 +15,7 @@ import argparse import logging +import math from pathlib import Path from crane_controller.crane_factory import build_crane @@ -94,8 +95,9 @@ def main() -> None: # noqa: PLR0915 _ = parser.add_argument( "--seed", type=int, - default=None, - help="Random seed for PPO initialisation. Omit for non-deterministic training.", + default=config.training.seed, + help="Random seed for PPO initialisation (default from --config, or None for " + "non-deterministic training). Pass explicitly to override.", ) _ = parser.add_argument( "--ent-coef", @@ -153,8 +155,32 @@ def main() -> None: # noqa: PLR0915 default=config.training.max_episode_steps, help="TimeLimit cap per episode (default 1000).", ) + _ = parser.add_argument( + "--acc", + type=float, + default=config.training.acc, + help="Crane acceleration magnitude in [-acc, +acc] (default 0.1). Continuous-action " + "training chatters and never settles at 0.1; try 0.01 for genuine rest " + "(see project_ppo_acc_squared_bug.md).", + ) + _ = parser.add_argument( + "--log-std-init", + type=float, + default=config.training.log_std_init, + help="Initial log std for the continuous-action Gaussian policy. Default: " + "auto-derived as log(acc/2.5) so ~99%% of raw samples start inside the action " + "box (avoids clip-dominated early training, see project_ppo_acc_squared_bug.md). " + "Pass explicitly to override, e.g. 0.0 for SB3's own default (std=1.0). Only " + "affects fresh training, not --resume-from.", + ) args = parser.parse_args() + # Auto-derive log_std_init from acc when not explicitly set, so it stays correctly + # sized for whatever acc this run uses (important once acc is swept/randomized across + # experiments, not just fixed at 0.1) — see project_ppo_acc_squared_bug.md. + if args.log_std_init is None and args.continuous_actions: + args.log_std_init = math.log(args.acc / 2.5) + # Resolve final reward config: explicit --reward-fac beats loaded YAML/defaults. reward_config = RewardConfig(*args.reward_fac) if args.reward_fac is not None else config.reward @@ -175,6 +201,8 @@ def main() -> None: # noqa: PLR0915 start_speed=args.start_speed, continuous_actions=args.continuous_actions, max_episode_steps=args.max_episode_steps, + acc=args.acc, + log_std_init=args.log_std_init, ), config_source=pre_args.config, ) @@ -219,6 +247,7 @@ def main() -> None: # noqa: PLR0915 gamma=args.gamma, save_path=args.save_path, continuous_actions=args.continuous_actions, + acc=args.acc, ), config_source=pre_args.config, ) @@ -236,6 +265,7 @@ def main() -> None: # noqa: PLR0915 rail_limit=args.rail_limit, reward_limit=resume_config.training.reward_limit, continuous_actions=args.continuous_actions, + acc=resume_config.training.acc, ), }, save_path=args.save_path, @@ -263,6 +293,7 @@ def main() -> None: # noqa: PLR0915 rail_limit=experiment_config.training.rail_limit, reward_limit=experiment_config.training.reward_limit, continuous_actions=args.continuous_actions, + acc=experiment_config.training.acc, ), }, save_path=args.save_path, @@ -273,6 +304,7 @@ def main() -> None: # noqa: PLR0915 clip_range=args.clip_range, n_steps=args.n_steps, max_episode_steps=experiment_config.training.max_episode_steps, + log_std_init=experiment_config.training.log_std_init, ) csv_path = str(Path(args.save_path).with_name(Path(args.save_path).stem + "_log.csv")) agent.do_training(args.steps, csv_path=csv_path) diff --git a/src/crane_controller/envs/controlled_crane_pendulum.py b/src/crane_controller/envs/controlled_crane_pendulum.py index 1e2b290..b28e120 100644 --- a/src/crane_controller/envs/controlled_crane_pendulum.py +++ b/src/crane_controller/envs/controlled_crane_pendulum.py @@ -522,7 +522,7 @@ def reset( self._reward_point = self._reward_plot_init("b.") self.nresets = -1 - else: # reset between episodes. Data are available + elif self.rewards: # reset between episodes, only if data is available self.reward_stats_calc(len(self.rewards)) if self.conf.render_mode == "data": # plot reward per step x = self.reward_stats["steps"] @@ -572,7 +572,9 @@ def step( (observation, reward, terminated, truncated, info) """ if self.conf.continuous_actions: - acc = float(np.asarray(action).flat[0]) * self.conf.acc + # action_space bounds are already ±self.conf.acc (physical units), so the + # sampled action is the acceleration directly — do not rescale by acc again. + acc = float(np.asarray(action).flat[0]) else: action_idx = int(action) if action_idx not in self.action_to_acc: @@ -610,7 +612,9 @@ def render(self, save_path: str | None = None) -> None: ---------- save_path : str or None, optional If set and render_mode is ``"plot"``, save the figure to this path - instead of showing it interactively (default None). + instead of showing it interactively (default None). Trajectory + traces are only recorded while ``render_mode == "plot"``, so that + mode is required for a figure to be produced at all. """ if self.conf.render_mode == "play-back": self.show_animation() diff --git a/src/crane_controller/experiment_config.py b/src/crane_controller/experiment_config.py index fa2c031..637dab6 100644 --- a/src/crane_controller/experiment_config.py +++ b/src/crane_controller/experiment_config.py @@ -160,6 +160,21 @@ class TrainingConfig: 100). Replaces the previous hardcoded value of 3000; shorter episodes let the discount factor propagate rail-penalty credit meaningfully (``0.99^100 ≈ 0.37`` vs ``0.99^3000 ≈ 10^-13``). + acc : float + Crane acceleration magnitude in ``[-acc, +acc]`` (default 0.1, matching + Sig's Q-learning agent). Continuous-action training at this value used to + converge to a persistent bang-bang chatter that never settled; the fix is + ``log_std_init`` (see below), not shrinking this value - ``0.1`` is fine + to leave at its default. See ``project_ppo_acc_squared_bug.md`` for the + full investigation, including a weaker-actuator alternative that also + works but sacrifices control authority. + log_std_init : float or None + Initial log standard deviation for the continuous-action Gaussian policy + (default None - auto-derived from ``acc`` as ``log(acc / 2.5)`` by + ``train_ppo.py`` so ~99% of raw samples start inside the action box; see + ``ProximalPolicyOptimizationAgent``'s docstring and + ``project_ppo_acc_squared_bug.md``). Pass explicitly to override, e.g. + ``0.0`` for SB3's own default (std=1.0). """ steps: int = 100_000 @@ -176,7 +191,9 @@ class TrainingConfig: start_speed: float = 1.0 continuous_actions: bool = True reward_limit: float = 50.0 + log_std_init: float | None = None max_episode_steps: int = 1000 + acc: float = 0.1 @classmethod def from_dict(cls, d: Mapping[str, object]) -> TrainingConfig: @@ -194,6 +211,7 @@ def from_dict(cls, d: Mapping[str, object]) -> TrainingConfig: """ defaults = cls() seed_raw = d.get("seed", defaults.seed) + log_std_init_raw = d.get("log_std_init", defaults.log_std_init) return cls( steps=int(d.get("steps", defaults.steps)), # type: ignore[arg-type,call-overload] n_envs=int(d.get("n_envs", defaults.n_envs)), # type: ignore[arg-type,call-overload] @@ -210,6 +228,8 @@ def from_dict(cls, d: Mapping[str, object]) -> TrainingConfig: continuous_actions=bool(d.get("continuous_actions", defaults.continuous_actions)), reward_limit=float(d.get("reward_limit", defaults.reward_limit)), # type: ignore[arg-type] max_episode_steps=int(d.get("max_episode_steps", defaults.max_episode_steps)), # type: ignore[arg-type,call-overload] + acc=float(d.get("acc", defaults.acc)), # type: ignore[arg-type] + log_std_init=float(log_std_init_raw) if isinstance(log_std_init_raw, (int, float)) else None, ) diff --git a/src/crane_controller/ppo_agent.py b/src/crane_controller/ppo_agent.py index 4d58316..0412854 100644 --- a/src/crane_controller/ppo_agent.py +++ b/src/crane_controller/ppo_agent.py @@ -108,6 +108,17 @@ class ProximalPolicyOptimizationAgent: Timesteps collected per environment before each gradient update (default 2048). Increasing to 8192 gives ~11 complete episodes per update instead of ~3, producing more stable gradient estimates for long-horizon tasks. + log_std_init : float or None, optional + Initial log standard deviation for the continuous-action Gaussian policy + (default None - use SB3's default of 0.0, i.e. initial std=1.0). SB3's + default is far wider than the action box at any `conf.acc` (box + half-width = `conf.acc`, so std=1.0 dwarfs it regardless of magnitude), + so nearly every raw sampled action clips to the boundary before training + has shaped anything - the root cause of a persistent bang-bang chatter + that never settled (see project_ppo_acc_squared_bug.md). Rule of thumb: + set this to ``log(box_half_width / 2.5)`` so ~99% of raw samples land + inside the box. `train_ppo.py` auto-derives this from `acc` when not + set explicitly. """ def __init__( # noqa: PLR0913,PLR0917 @@ -123,6 +134,7 @@ def __init__( # noqa: PLR0913,PLR0917 learning_rate: float = 3e-4, clip_range: float = 0.2, n_steps: int = 2048, + log_std_init: float | None = None, ) -> None: """Set up the agent for training. Use :meth:`load` for inference.""" self.save_path = save_path @@ -134,6 +146,7 @@ def __init__( # noqa: PLR0913,PLR0917 env_kwargs=env_kwargs, ) self.vec_env = VecNormalize(raw_vec_env, norm_obs=True, norm_reward=True) + policy_kwargs = {"log_std_init": log_std_init} if log_std_init is not None else None self.model = PPO( "MlpPolicy", self.vec_env, @@ -143,6 +156,7 @@ def __init__( # noqa: PLR0913,PLR0917 learning_rate=learning_rate, clip_range=clip_range, n_steps=n_steps, + policy_kwargs=policy_kwargs, verbose=1 if n_envs == 1 else 0, ) self.env: AntiPendulumEnv = self.vec_env.venv.envs[0] # type: ignore[attr-defined] @@ -398,7 +412,9 @@ def do_one_episode( env_u = self.env.unwrapped energy_final = 0.5 * float(env_u.wire.cm_v[0]) ** 2 # type: ignore[attr-defined] if env_u.conf.continuous_actions: # type: ignore[attr-defined] - acc_final = float(np.asarray(last_action).flat[0]) * float(env_u.conf.acc) # type: ignore[attr-defined] + # action_space bounds are already ±conf.acc (physical units) — same as + # step()'s continuous branch, do not rescale by acc again. + acc_final = float(np.asarray(last_action).flat[0]) else: acc_final = float(env_u.action_to_acc[int(last_action)]) # type: ignore[attr-defined] return EpisodeResult( diff --git a/tests/test_environment.py b/tests/test_environment.py index dd2c755..35a375b 100644 --- a/tests/test_environment.py +++ b/tests/test_environment.py @@ -256,6 +256,24 @@ def test_step_accepts_correct_action(crane: Callable[..., Crane], continuous_act assert obs.shape == (4,) +def test_continuous_action_applies_full_acceleration(crane: Callable[..., Crane]) -> None: + """step()'s applied acceleration matches conf.acc at the action-space boundary. + + Regression test: continuous Box bounds are ``(-conf.acc, conf.acc)`` (physical units), + so a boundary action must produce ``d_velocity == conf.acc``, not ``conf.acc ** 2`` + from an extra, redundant scaling in step(). + """ + conf = AntiPendulumConfig(continuous_actions=True, acc=0.4) + env = AntiPendulumEnv(crane, conf=conf) + _ = env.reset(options={"init": True}) + action = np.array([conf.acc], dtype=np.float32) # boundary of the action space + _ = env.step(action) + applied_acc = env.crane.d_velocity[0] + assert abs(applied_acc - conf.acc) < 1e-6, ( + f"Expected acceleration {conf.acc}, got {applied_acc} (acc**2 would be {conf.acc**2})" + ) + + def test_discrete_observations(crane: Callable[..., Crane], *, show: bool): """Test the transitions with respect to categories in the discrete observation space.""" env = AntiPendulumEnv(crane, conf=AntiPendulumConfig(continuous_actions=False, discrete="phase"))