Optimistic Distributionally Robust Policy Optimization
This addresses a key limitation in reinforcement learning for domains like robotics, though it appears incremental as it builds on existing methods.
The paper tackles the problem of policy-based reinforcement learning methods like TRPO and PPO converging to sub-optimal solutions due to parametric distribution limitations, and it introduces the ODRPO algorithm, which improves sample efficiency and final policy performance while achieving globally optimal policy updates.
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.