Back-Stepping Experience Replay With Application to Model-Free Reinforcement Learning for a Soft Snake Robot
Xinda Qi, Dong Chen, Zhaojian Li, Xiaobo Tan
Abstract
In this letter, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms. BER aims to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. The method constructs reversed trajectories using back-stepping transitions to reach random or fixed targets. Interpretable as a bi-directional approach, BER addresses inaccuracies in back-stepping transitions through a purification of the replay experience during learning. Given the intricate nature of soft robots and their complex interactions with environments, we present an application of BER in a model-free RL approach for the locomotion and navigation of a soft snake robot, which is capable of serpentine motion enabled by anisotropic friction between the body and ground. In addition, a dynamic simulator is developed to assess the effectiveness and efficiency of the BER algorithm, in which the robot demonstrates successful learning (reaching a 100% success rate) and adeptly reaches random targets, achieving an average speed 48% faster than that of the best baseline approach.
BibTeX
@inproceedings{ral2024_backsteppingexpe,
title = {Back-Stepping Experience Replay With Application to Model-Free Reinforcement Learning for a Soft Snake Robot},
author = {Xinda Qi and Dong Chen and Zhaojian Li and Xiaobo Tan},
booktitle = {RA-L 2024},
year = {2024}
}