Reinforcement Learning for Self-Healing Material Systems

2025-11-23

AI Summary

This study addresses the dynamic trade-off between structural integrity maintenance and limited repair resource consumption in self-healing material systems. We propose an adaptive control framework based on reinforcement learning (RL) with continuous action spaces. The repair process is formalized as a Markov decision process, and controllers are trained in stochastic simulation environments using advanced RL algorithms—specifically Twin Delayed Deep Deterministic Policy Gradient (TD3)—to enable fine-grained, continuous regulation of repair dosage. Compared to conventional discrete-action methods and heuristic strategies, our framework achieves significantly faster convergence, enhanced control stability, and superior repair efficiency, experimentally demonstrating near-complete functional recovery of the material. The core contribution lies in the first systematic integration of continuous-control RL into dynamic self-healing material systems, empirically validating its feasibility and superiority in autonomously optimizing material lifetime under resource constraints.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Developing adaptive control for autonomous self-healing material systems
• Balancing structural integrity maintenance with finite resource consumption
• Optimizing continuous dosage control for efficient material recovery

Innovation

• Framing self-healing as a Reinforcement Learning problem
• Comparing discrete and continuous-action agents for control
• Using continuous dosage control for superior stability