Decaying Clipping Range in Proximal Policy Optimization

TL;DR

This paper introduces decaying clipping range strategies in PPO to enhance exploration early on and enforce stricter policy updates later, improving performance in various reinforcement learning environments.

Contribution

It proposes simple, effective decaying clipping methods for PPO, offering an alternative to constant clipping to improve learning dynamics.

Findings

Decaying clipping improves reward outcomes in several environments.

Decaying strategies influence exploration and policy restriction balance.

Effective alternative to constant clipping in PPO.

Abstract

Proximal Policy Optimization (PPO) is among the most widely used algorithms in reinforcement learning, which achieves state-of-the-art performance in many challenging problems. The keys to its success are the reliable policy updates through the clipping mechanism and the multiple epochs of minibatch updates. The aim of this research is to give new simple but effective alternatives to the former. For this, we propose linearly and exponentially decaying clipping range approaches throughout the training. With these, we would like to provide higher exploration at the beginning and stronger restrictions at the end of the learning phase. We investigate their performance in several classical control and locomotive robotic environments. During the analysis, we found that they influence the achieved rewards and are effective alternatives to the constant clipping method in many reinforcement learning tasks.