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arXiv · 2506.16590

Energy-Based Transfer for Reinforcement Learning

Zeyun Deng·Jasorsi Ghosh·Fiona Xie·Yuzhe Lu·Katia Sycara·Joseph Campbell
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Abstract

Reinforcement learning algorithms often suffer from poor sample efficiency, making them challenging to apply in multi-task or continual learning settings. Efficiency can be improved by transferring knowledge from a previously trained teacher policy to guide exploration in new but related tasks. However, if the new task sufficiently differs from the teacher's training task, the transferred guidance may be sub-optimal and bias exploration toward low-reward behaviors. We propose an energy-based transfer learning method that uses out-of-distribution detection to selectively issue guidance, enabling the teacher to intervene only in states within its training distribution. We theoretically show that energy scores reflect the teacher's state-visitation density and empirically demonstrate improved sample efficiency and performance across both single-task and multi-task settings.

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Zeyun Deng, Jasorsi Ghosh, Fiona Xie, Yuzhe Lu, Katia Sycara, Joseph Campbell. 2025-06-19. Energy-Based Transfer for Reinforcement Learning. https://arxiv.org/abs/2506.16590

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