Beyond the Boundaries of Proximal Policy Optimization

ICLR 2025 Conference Submission12312 Authors

Keywords: Reinforcement learning, optimization, proximal policy optimization

TL;DR: We consider proximal policy optimization to estimate update vectors, on which arbitrary gradient-based optimizers can be applied.

Abstract: Proximal policy optimization (PPO) is a widely-used algorithm for on-policy reinforcement learning. This work offers an alternative perspective of PPO, in which it is decomposed into the inner-loop estimation of update vectors, and the outer-loop application of updates using gradient ascent with unity learning rate. Using this insight we propose outer proximal policy optimization (outer-PPO); a framework wherein these update vectors are applied using an arbitrary gradient-based optimizer. The decoupling of update estimation and update application enabled by outer-PPO highlights several implicit design choices in PPO that we challenge through empirical investigation. In particular we consider non-unity learning rates and momentum applied to the outer loop, and a momentum-bias applied to the inner estimation loop. Methods are evaluated against an aggressively tuned PPO baseline on Brax, Jumaji and MinAtar environments; non-unity learning rates and momentum both achieve statistically significant improvement on Brax and Jumaji, given the same hyperparameter tuning budget.

Primary Area: reinforcement learning

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Submission Number: 12312