Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot

• URL: http://arxiv.org/abs/2401.11372v2
• Date: Mon, 23 Sep 2024 22:58:39 GMT
• Title: Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot
• Authors: Xinda Qi, Dong Chen, Zhaojian Li, Xiaobo Tan,
• Abstract summary: Back-stepping Experience Replay (BER) is compatible with arbitrary off-policy reinforcement learning algorithms. We present an application of BER in a model-free RL approach for the locomotion and navigation of a soft snake robot.
• Score: 15.005962159112002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, 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 distillation 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.

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