Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

2026-05-29 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Researchers have introduced ReMax, a novel objective for reinforcement learning (RL) agents designed to induce exploration without explicit bonus terms. ReMax evaluates a policy by the expected maximum return over M samples, a positive integer, while also considering return uncertainty. This formalization allows stochastic exploration to emerge as an inherent property. To facilitate efficient policy optimization, a new policy-gradient formulation for ReMax was derived. This led to ReMax PPO (RePPO), a variant of PPO that optimizes the ReMax objective. RePPO further enhances control by generalizing the discrete retry count M to a continuous parameter m > 0. Empirical evaluations demonstrate that RePPO effectively promotes exploration on the MinAtar and Craftax benchmarks, confirming its ability to achieve exploration without relying on traditional explicit exploration bonuses. The work was published on 2026-05-29.

Key takeaway

For Machine Learning Engineers designing reinforcement learning agents, consider adopting RePPO to achieve effective exploration without the complexity of explicit bonus terms. This approach allows you to fine-tune exploration behavior using a continuous parameter "m". This can simplify agent design and improve performance on benchmarks like MinAtar and Craftax. You can integrate this PPO variant to streamline exploration strategies in your RL projects.

Key insights

ReMax induces emergent stochastic exploration in RL by optimizing for expected maximum return over retries, eliminating explicit bonuses.

Principles

• Exploration emerges from optimizing for retries.
• Greedy policies are optimal without retries.
• Continuous retry parameter offers fine-grained control.

Method

RePPO optimizes the ReMax objective using a new policy-gradient formulation, generalizing the discrete retry count M to a continuous parameter m > 0 for fine-grained exploration control.

In practice

• Apply RePPO for exploration in RL.
• Use continuous "m" for exploration tuning.
• Benchmark RePPO on MinAtar or Craftax.

Topics

• Reinforcement Learning
• Policy Gradient
• Exploration Strategies
• ReMax Objective
• RePPO
• MinAtar Benchmark
• Craftax Benchmark

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.