Reinforcement Learning for Self-Healing Material Systems

• URL: http://arxiv.org/abs/2511.18728v1
• Date: Mon, 24 Nov 2025 03:42:00 GMT
• Title: Reinforcement Learning for Self-Healing Material Systems
• Authors: Maitreyi Chatterjee, Devansh Agarwal, Biplab Chatterjee,
• Abstract summary: This study frames the self-healing process as a Reinforcement Learning problem within a Markov Decision Process (MDP), enabling agents to autonomously derive optimal policies.<n>A comparative evaluation of discrete-action (Q-learning, DQN) and continuous-action (TD3) agents in a simulation environment revealed that RL controllers significantly outperform baselines, achieving near-complete material recovery.
• Score: 0.40334315349753025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transition to autonomous material systems necessitates adaptive control methodologies to maximize structural longevity. This study frames the self-healing process as a Reinforcement Learning (RL) problem within a Markov Decision Process (MDP), enabling agents to autonomously derive optimal policies that efficiently balance structural integrity maintenance against finite resource consumption. A comparative evaluation of discrete-action (Q-learning, DQN) and continuous-action (TD3) agents in a stochastic simulation environment revealed that RL controllers significantly outperform heuristic baselines, achieving near-complete material recovery. Crucially, the TD3 agent utilizing continuous dosage control demonstrated superior convergence speed and stability, underscoring the necessity of fine-grained, proportional actuation in dynamic self-healing applications.

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