Zero-Shot Reinforcement Learning Under Partial Observability

Scott Jeen; Tom Bewley; Jonathan M. Cullen

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arXiv · 2506.15446

Zero-Shot Reinforcement Learning Under Partial Observability

Scott Jeen·Tom Bewley·Jonathan M. Cullen

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Abstract

Recent work has shown that, under certain assumptions, zero-shot reinforcement learning (RL) methods can generalise to any unseen task in an environment after reward-free pre-training. Access to Markov states is one such assumption, yet, in many real-world applications, the Markov state is only partially observable. Here, we explore how the performance of standard zero-shot RL methods degrades when subjected to partially observability, and show that, as in single-task RL, memory-based architectures are an effective remedy. We evaluate our memory-based zero-shot RL methods in domains where the states, rewards and a change in dynamics are partially observed, and show improved performance over memory-free baselines. Our code is open-sourced via: https://enjeeneer.io/projects/bfms-with-memory/.

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BibTeX RIS

Scott Jeen, Tom Bewley, Jonathan M. Cullen. 2025-06-18. Zero-Shot Reinforcement Learning Under Partial Observability. https://arxiv.org/abs/2506.15446

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