Optimistic Distributionally Robust Policy Optimization

• URL: http://arxiv.org/abs/2006.07815v1
• Date: Sun, 14 Jun 2020 06:36:18 GMT
• Title: Optimistic Distributionally Robust Policy Optimization
• Authors: Jun Song, Chaoyue Zhao
• Abstract summary: Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are prone to converge to a sub-optimal solution as they limit policy representation to a particular parametric distribution class. We develop an innovative Optimistic Distributionally Robust Policy Optimization (ODRO) algorithm to solve the trust region constrained optimization problem without parameterizing the policies. Our algorithm improves TRPO and PPO with a higher sample efficiency and a better performance of the final policy while attaining the learning stability.
• Score: 2.345728642535161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), as the widely employed policy based reinforcement learning (RL) methods, are prone to converge to a sub-optimal solution as they limit the policy representation to a particular parametric distribution class. To address this issue, we develop an innovative Optimistic Distributionally Robust Policy Optimization (ODRPO) algorithm, which effectively utilizes Optimistic Distributionally Robust Optimization (DRO) approach to solve the trust region constrained optimization problem without parameterizing the policies. Our algorithm improves TRPO and PPO with a higher sample efficiency and a better performance of the final policy while attaining the learning stability. Moreover, it achieves a globally optimal policy update that is not promised in the prevailing policy based RL algorithms. Experiments across tabular domains and robotic locomotion tasks demonstrate the effectiveness of our approach.

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