SAMBA: Safe Model-Based & Active Reinforcement Learning

• URL: http://arxiv.org/abs/2006.09436v1
• Date: Fri, 12 Jun 2020 10:40:46 GMT
• Title: SAMBA: Safe Model-Based & Active Reinforcement Learning
• Authors: Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Abdullah, Aivar Sootla, Jun Wang, Haitham Ammar
• Abstract summary: SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
• Score: 59.01424351231993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our method builds upon PILCO to enable active exploration using novel(semi-)metrics for out-of-sample Gaussian process evaluation optimised through a multi-objective problem that supports conditional-value-at-risk constraints. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. Our results show orders of magnitude reductions in samples and violations compared to state-of-the-art methods. Lastly, we provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.

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