Sotetsu Koyamada

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

ReMax PPO (RePPO) is introduced, a PPO variant that optimizes ReMax while generalizing the discrete retry count $M$ to a continuous parameter $m>0$, enabling fine-grained control of exploration.