Reservoir Computing Encodes Physical Adaptations for Reinforcement Learning

Abstract

Adapting reinforcement learning (RL) policies to various robot body configurations is a significant challenge for creating flexible autonomous systems. This study presents a novel framework that integrates Reservoir Computing (RC) with the First-Order Reduced and Controlled Error (FORCE) learning rule to enhance policy adaptability in RL. The RC serves as a dynamic feature extractor, capturing temporal dependencies by pre-training on state transitions generated through random actions. This pre-training acts as regularization, reducing variance and preventing overfitting to specific configurations Subsequently, the control policy network is trained on a limited set of body variations using the enriched features from the RC. Experimental results across three distinct environments demonstrate that the proposed RC+FORCE framework significantly improves policy performance and adaptability to unseen robot configurations compared to traditional reinforcement learning through domain randomization. These findings highlight the effectiveness of combining RC-based feature extraction with FORCE-based training in developing robust RL agents.