arXiv:2606.05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses. In discrete action spaces, ReMax was shown to do so by adapting to return uncertainty. In this work, we introduce pathwise derivative estimators for retry objectives and use them to extend ReMax to continuous action spaces. We study the resulting learning dynamics and show that, even with deterministic rewards, ReMax can encourage sto
The continuous drive for more robust and exploratory reinforcement learning algorithms is pushing research into advanced optimization techniques for complex action spaces.
Improving AI's ability to explore and make decisions in continuous environments is crucial for advancements in robotics, autonomous systems, and generative AI across various industries.
The extension of retry-based objectives to continuous action spaces opens new avenues for training more effective and adaptable AI agents, particularly in domains previously constrained by exploration limitations.
- · AI/ML researchers and developers
- · Robotics industry
- · Autonomous systems developers
- · Reinforcement learning platforms
- · Traditional RL exploration methods (if less effective)
AI agents in continuous environments will exhibit more sophisticated and exploratory behaviors, leading to faster learning and better performance.
This improved exploration capability could accelerate the development of general-purpose AI and more versatile autonomous agents across various applications.
Enhanced AI agent capabilities based on these techniques could further blur the lines between human and machine decision-making in complex operational settings.
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Read at arXiv cs.AI