Trust Region-Guided Proximal Policy Optimization

TL;DR

This paper introduces Trust Region-Guided PPO, a novel reinforcement learning algorithm that adaptively adjusts policy clipping to enhance exploration and performance, especially in challenging initial conditions.

Contribution

We propose TRGPPO, which adaptively adjusts the clipping range within the trust region, improving exploration and theoretical performance bounds over standard PPO.

Findings

TRGPPO outperforms PPO in various tasks.

Enhanced exploration reduces training failures.

Theoretical analysis shows better performance bounds.

Abstract

Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, as a model-free RL method, the success of PPO relies heavily on the effectiveness of its exploratory policy search. In this paper, we give an in-depth analysis on the exploration behavior of PPO, and show that PPO is prone to suffer from the risk of lack of exploration especially under the case of bad initialization, which may lead to the failure of training or being trapped in bad local optima. To address these issues, we proposed a novel policy optimization method, named Trust Region-Guided PPO (TRGPPO), which adaptively adjusts the clipping range within the trust region. We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well. Extensive experiments verify the advantage of the proposed method.