# Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency

@inproceedings{Cai2022ReinforcementLF, title={Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency}, author={Qi Cai and Zhuoran Yang and Zhaoran Wang}, booktitle={ICML}, year={2022} }

We study reinforcement learning for partially observed Markov decision processes (POMDPs) with infinite observation and state spaces, which remains less investigated theoretically. To this end, we make the first attempt at bridging partial observability and function approximation for a class of POMDPs with a linear structure. In detail, we propose a reinforcement learning algorithm (Optimistic Exploration via Adversarial Integral Equation or OP-TENET) that attains an ǫ-optimal policy within O(1…

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