Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

📰 ArXiv cs.AI

Maximum entropy behavior exploration improves zero-shot reinforcement learning by generating diverse pretraining datasets

advanced Published 27 Mar 2026

Action Steps

Collect a reward-free dataset using maximum entropy behavior exploration
Use the collected dataset to pretrain a family of policies
Recover optimal policies for any reward function at test time
Evaluate the performance of the recovered policies across tasks

Who Needs to Know This

Researchers and engineers working on reinforcement learning and robotics can benefit from this approach to improve the performance of their models in real-world environments

Key Insight

💡 Maximum entropy behavior exploration can generate diverse pretraining datasets for zero-shot reinforcement learning

Key Takeaways

Maximum entropy behavior exploration improves zero-shot reinforcement learning by generating diverse pretraining datasets

Full Article

Title: Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

Abstract:
arXiv:2603.25464v1 Announce Type: cross Abstract: Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies for any reward function directly at test time. Naturally, the quality of the pretraining dataset determines the performance of the recovered policies across tasks. However, pre-collecting a relevant, diverse dataset without prior knowledge of the downstream tasks of interest remains a challenge. In this work,

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