Constrained Variational Policy Optimization for Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2201.11927v1
• Date: Fri, 28 Jan 2022 04:24:09 GMT
• Title: Constrained Variational Policy Optimization for Safe Reinforcement Learning
• Authors: Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao
• Abstract summary: Safe reinforcement learning aims to learn policies that satisfy certain constraints before deploying to safety-critical applications. primal-dual as a prevalent constrained optimization framework suffers from instability issues and lacks optimality guarantees. This paper overcomes the issues from a novel probabilistic inference perspective and proposes an Expectation-Maximization style approach to learn safe policy.
• Score: 40.38842532850959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying to safety-critical applications. Primal-dual as a prevalent constrained optimization framework suffers from instability issues and lacks optimality guarantees. This paper overcomes the issues from a novel probabilistic inference perspective and proposes an Expectation-Maximization style approach to learn safe policy. We show that the safe RL problem can be decomposed to 1) a convex optimization phase with a non-parametric variational distribution and 2) a supervised learning phase. We show the unique advantages of constrained variational policy optimization by proving its optimality and policy improvement stability. A wide range of experiments on continuous robotic tasks show that the proposed method achieves significantly better performance in terms of constraint satisfaction and sample efficiency than primal-dual baselines.

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