Meta-Model-Based Meta-Policy Optimization

• URL: http://arxiv.org/abs/2006.02608v5
• Date: Mon, 11 Oct 2021 11:59:10 GMT
• Title: Meta-Model-Based Meta-Policy Optimization
• Authors: Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
• Abstract summary: We propose a model-based meta-reinforcement learning (RL) method with a performance guarantee. We demonstrate that M3PO outperforms existing meta-RL methods in continuous-control benchmarks.
• Score: 19.468989399627638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings. However, the theoretical understanding of those methods is yet to be established, and there is currently no theoretical guarantee of their performance in a real-world environment. In this paper, we analyze the performance guarantee of model-based meta-RL methods by extending the theorems proposed by Janner et al. (2019). On the basis of our theoretical results, we propose Meta-Model-Based Meta-Policy Optimization (M3PO), a model-based meta-RL method with a performance guarantee. We demonstrate that M3PO outperforms existing meta-RL methods in continuous-control benchmarks.

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