Improving generalization of robot locomotion policies via Sharpness-Aware Reinforcement Learning

• URL: http://arxiv.org/abs/2411.19732v1
• Date: Fri, 29 Nov 2024 14:25:54 GMT
• Title: Improving generalization of robot locomotion policies via Sharpness-Aware Reinforcement Learning
• Authors: Severin Bochem, Eduardo Gonzalez-Sanchez, Yves Bicker, Gabriele Fadini,
• Abstract summary: Differentiable simulators offer improved sample efficiency through exact gradients, but can be unstable in contact-rich environments. This paper introduces a novel approach integrating sharpness-aware optimization into gradient-based reinforcement learning algorithms.
• Score: 0.5399800035598186
• License:
• Abstract: Reinforcement learning often requires extensive training data. Simulation-to-real transfer offers a promising approach to address this challenge in robotics. While differentiable simulators offer improved sample efficiency through exact gradients, they can be unstable in contact-rich environments and may lead to poor generalization. This paper introduces a novel approach integrating sharpness-aware optimization into gradient-based reinforcement learning algorithms. Our simulation results demonstrate that our method, tested on contact-rich environments, significantly enhances policy robustness to environmental variations and action perturbations while maintaining the sample efficiency of first-order methods. Specifically, our approach improves action noise tolerance compared to standard first-order methods and achieves generalization comparable to zeroth-order methods. This improvement stems from finding flatter minima in the loss landscape, associated with better generalization. Our work offers a promising solution to balance efficient learning and robust sim-to-real transfer in robotics, potentially bridging the gap between simulation and real-world performance.

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