Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying 事件

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying arXiv:2606.00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can improve performance or reduce uncertainty; without such retries, a greedy policy is optimal. We formalize this intuition with ReMax, an objective that evaluates a policy by the expected maximum return over $M$ samples, wh