Reinforcement Learning for Self-Healing Material Systems

TL;DR

This paper applies reinforcement learning to autonomous self-healing materials, enabling agents to optimize repair actions and resource use, resulting in improved recovery and stability compared to heuristic methods.

Contribution

It introduces a novel RL framework for self-healing materials, comparing discrete and continuous control strategies, and demonstrates the effectiveness of continuous dosage control for material recovery.

Findings

RL controllers outperform heuristics in recovery efficiency

TD3 agent shows faster convergence and stability

Continuous control enables fine-grained actuation

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.