Safe Reinforcement Learning by Imagining the Near FutureDownload PDF

May 21, 2021 (edited Jan 21, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: safe reinforcement learning, model-based reinforcement learning
  • TL;DR: We devise a short-horizon model-based reinforcement learning algorithm to avoid safety violations by penalizing unsafe trajectories.
  • Abstract: Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative consequences. In this work, we focus on the setting where unsafe states can be avoided by planning ahead a short time into the future. In this setting, a model-based agent with a sufficiently accurate model can avoid unsafe states. We devise a model-based algorithm that heavily penalizes unsafe trajectories, and derive guarantees that our algorithm can avoid unsafe states under certain assumptions. Experiments demonstrate that our algorithm can achieve competitive rewards with fewer safety violations in several continuous control tasks.
  • Supplementary Material: zip
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  • Code: https://github.com/gwthomas/Safe-MBPO
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