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تسجيل مستخدم جديدProvably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
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We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: batch learning model and rare policy switch model, and propose two efficient online RL algorithms for linear Markov decision processes. In specific, for the batch learning model, our proposed LSVI-UCB-Batch algorithm achieves an $tilde O(sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature mapping, $H$ is the episode length, $T$ is the number of interactions and $B$ is the number of batches. Our result suggests that it suffices to use only $sqrt{T/dH}$ batches to obtain $tilde O(sqrt{d^3H^3T})$ regret. For the rare policy switch model, our proposed LSVI-UCB-RareSwitch algorithm enjoys an $tilde O(sqrt{d^3H^3T[1+T/(dH)]^{dH/B}})$ regret, which implies that $dHlog T$ policy switches suffice to obtain the $tilde O(sqrt{d^3H^3T})$ regret. Our algorithms achieve the same regret as the LSVI-UCB algorithm (Jin et al., 2019), yet with a substantially smaller amount of adaptivity.
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