Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

Researchers introduce ReMax, a reinforcement learning objective that naturally induces exploration by evaluating policies over multiple samples, and develop RePPO, a PPO variant that achieves exploration without explicit bonus terms. The approach generalizes discrete retry counts to a continuous parameter, enabling fine-grained control of exploration in policy gradient methods.

This research addresses a fundamental challenge in reinforcement learning: how exploration emerges naturally from policy optimization. Traditionally, RL algorithms require explicit exploration bonuses or epsilon-greedy strategies to encourage agents to try different actions. The ReMax framework reframes exploration as an emergent property by optimizing for the expected maximum return across multiple environment interactions with the same policy, accounting for uncertainty in returns. This insight reveals that repeated interactions with similar states naturally incentivize stochastic behavior without artificial incentives.

The development of RePPO extends this concept into practical implementation by deriving a policy-gradient formulation suitable for PPO optimization. By generalizing the discrete retry count M into a continuous parameter m, the method offers fine-grained control over exploration intensity, allowing practitioners to tune exploration-exploitation tradeoffs precisely. This represents a meaningful advancement in understanding how exploration mechanisms function within policy gradient methods, bridging theoretical insights with practical algorithms.

For the AI research community, this work has implications for autonomous systems, robotics, and game-playing agents where exploration efficiency directly impacts sample complexity and real-world deployment costs. The approach potentially reduces reliance on domain-specific exploration heuristics, enabling more general and transferable algorithms. Empirical validation on MinAtar and Craftax benchmarks demonstrates practical viability, though broader evaluation across continuous control and real-world domains remains important.

Future research should investigate how ReMax generalizes to off-policy settings, large-scale applications, and whether continuous parameter m scheduling improves performance in complex environments.

• →ReMax objective induces exploration as an emergent property without explicit bonus terms by optimizing expected maximum returns over multiple samples.
• →RePPO extends ReMax to practical policy gradient optimization using a continuous exploration parameter m for fine-grained control.
• →The framework demonstrates that repeated environment interactions naturally incentivize stochastic policies without artificial exploration incentives.
• →Empirical validation shows improved exploration on MinAtar and Craftax benchmarks compared to standard PPO approaches.
• →This research advances understanding of how exploration mechanisms function in policy gradient methods and may reduce domain-specific engineering requirements.