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vectorized-training

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tk-yasuno / dql-maintenance-faster

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Deep Q-Network (DQN) implementation for optimal maintenance planning of 100-bridge fleet infrastructure using advanced reinforcement learning techniques and vectorized parallel training.

reinforcement-learning dqn performance-optimization deep-q-learning predictive-maintenance cbm large-scale-optimization structural-health-monitoring parallel-training markov-decision-process prognostics-health-management bridge-maintenance disaster-resilience infrastructure-maintenance-management infrastructure-resilience bridge-fleet vectorized-training high-performance-rl
  • Updated Dec 6, 2025
  • Python

tk-yasuno / markov-dqn-vectorized

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Deep Q-Network implementation for optimal bridge maintenance planning using Markov Decision Process formulation with vectorized parallel training. Based on Phase 3 (Vectorized DQN) from dql-maintenance-faster project.

reinforcement-learning dqn performance-optimization deep-q-learning predictive-maintenance cbm large-scale-optimization structural-health-monitoring parallel-training markov-decision-process prognostics-health-management bridge-maintenance disaster-resilience infrastructure-maintenance-management infrastructure-resilience bridge-fleet vectorized-training
  • Updated Dec 8, 2025
  • Python

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