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This theoretical reinforcement learning paper studies how many interactions an agent needs to find a near-optimal policy when it learns from one continuous trajectory rather than sampling any state-action pair on demand. Its main claim is that, for a broad class of environments and at high accuracy, navigation between states adds no worst-case cost compared with unrestricted sampling, provided information gathering adapts to how the environment reveals information. Readers should treat this as a bound-based result on worst-case complexity, not evidence of practical performance gains.
Average-Reward Policy Identification from a Single Continuing Trajectory
arXiv:2610.20486v1 Announce Type: new Abstract: In reinforcement learning, an agent must explore an unknown environment to learn how to make good decisions. Unlike methods that can freely sample any state--action pair, an agent learning from a single trajectory must spend additional time navigating between states. It is therefore natural to expect that learning from a single trajectory requires more experience. In this paper, we show that this additional cost is not always necessary. For average-reward reinforcement learning, we develop an algorithm that learns where to collect information while ensuring that the required samples can be obtained efficiently. We establish an upper bound on the number of interactions needed to identify a near-optimal policy. Furthermore, we prove a matching information theoretic lower bound, showing that, for a broad class of environments and at sufficiently high accuracy, learning from a single trajectory is no harder in the worst case than learning with unrestricted access to state action samples. Our results reveal that the additional complexity cost of navigation can be avoided by adapting information gathering requirements to how the environment provides information.
Abstract:In reinforcement learning, an agent must explore an unknown environment to learn how to make good decisions. Unlike methods that can freely sample any state--action pair, an agent learning from a single trajectory must spend additional time navigating between states. It is therefore natural to expect that learning from a single trajectory requires more experience. In this paper, we show that this additional cost is not always necessary. For average-reward reinforcement learning, we develop an algorithm that learns where to collect information while ensuring that the required samples can be obtained efficiently. We establish an upper bound on the number of interactions needed to identify a near-optimal policy. Furthermore, we prove a matching information theoretic lower bound, showing that, for a broad class of environments and at sufficiently high accuracy, learning from a single trajectory is no harder in the worst case than learning with unrestricted access to state action samples. Our results reveal that the additional complexity cost of navigation can be avoided by adapting information gathering requirements to how the environment provides information.
Machine Learning (cs.LG)
arXiv:2610.20486 [cs.LG]
(or arXiv:2610.20486v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.20486 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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