Reinforcement Learning using Guided Observability

• URL: http://arxiv.org/abs/2104.10986v1
• Date: Thu, 22 Apr 2021 10:47:35 GMT
• Title: Reinforcement Learning using Guided Observability
• Authors: Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen
• Abstract summary: We propose a simple but efficient approach to make reinforcement learning cope with partial observability. Our main insight is that smoothly transitioning from full observability to partial observability during the training process yields a high performance policy. A comprehensive evaluation in discrete partially observableMarkov decision process (POMDP) benchmark problems and continuous partially observable MuJoCo and OpenAI gym tasks shows that PO-GRL improves performance.
• Score: 26.307025803058714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems. However, an open question is how to make RL cope with partial observability which is prevalent in many real-world problems. Contrary to contemporary RL approaches, which focus mostly on improved memory representations or strong assumptions about the type of partial observability, we propose a simple but efficient approach that can be applied together with a wide variety of RL methods. Our main insight is that smoothly transitioning from full observability to partial observability during the training process yields a high performance policy. The approach, called partially observable guided reinforcement learning (PO-GRL), allows to utilize full state information during policy optimization without compromising the optimality of the final policy. A comprehensive evaluation in discrete partially observableMarkov decision process (POMDP) benchmark problems and continuous partially observable MuJoCo and OpenAI gym tasks shows that PO-GRL improves performance. Finally, we demonstrate PO-GRL in the ball-in-the-cup task on a real Barrett WAM robot under partial observability.

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