Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability
arXiv:2510.03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization. Prior work established that statistically efficient learning is impossible for either of these problems when the only assumptions are that the data has good coverage (concentrability) and the state-action value function of every policy is linearly realizable ($q^\pi$-realizability) (Foster et al., 2021). Recently, Tkachuk et al. (2024) gave a statistically efficient learner for policy optimization, if in
This paper advances the theoretical understanding of statistically efficient policy evaluation in offline reinforcement learning, building upon recent breakthroughs in the field.
Improved statistical efficiency in offline reinforcement learning could accelerate the development of more reliable and data-efficient AI agents, particularly in domains where data collection is expensive or risky.
The prior impossibility result for statistically efficient learning in certain offline RL settings is being re-evaluated and partially overcome through new methodological approaches.
- · AI researchers
- · Reinforcement learning developers
- · Industries using offline RL (e.g., healthcare, finance)
- · Developers reliant on vast data for RL
More robust and efficient offline RL algorithms become feasible.
Faster iteration cycles for AI agent development without extensive real-world experimentation.
Broader adoption of sophisticated AI agents in safety-critical and data-scarce environments.
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