Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
Quick summary
arXiv:2606.00151v2 Announce Type: replace-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, where $M$ is a positive integer, while accounting for return uncertainty. Optimizing this objective induces stochastic exploration as an emergent property, without explicit bo
Key takeaways
- arXiv:2606.00151v2 Announce Type: replace-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, where $M$ is a positive integer, while accounting for return uncertainty.
- Optimizing this objective induces stochastic exploration as an emergent property, without explicit bo
Why it matters
“Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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