arXiv:2606.00151v1 Announce Type: new 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 bonus terms.
The paper addresses a fundamental challenge in reinforcement learning (exploration vs. exploitation) with a novel approach that doesn't rely on explicit bonus terms, reflecting ongoing advancements in AI research.
This research could lead to more efficient and robust reinforcement learning agents, impacting areas from autonomous systems to complex decision-making AI, by intrinsically improving exploration.
Traditional explicit exploration bonuses may become less necessary, simplifying policy optimization and potentially broadening the applicability of RL to new domains.
- · AI/ML researchers
- · Reinforcement learning applications
- · AI model developers
Reinforcement learning agents will become more effective at discovering optimal policies in complex environments.
This could accelerate the development of more capable AI agents across various industries, from logistics to robotics.
The intrinsic emergence of exploration might lead to less biased and more generalizable AI behaviors, impacting the trustworthiness of autonomous systems.
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Read at arXiv cs.LG