Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function ApproximationDownload PDF

Anonymous

22 Sept 2022, 12:37 (modified: 11 Nov 2022, 22:25)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: Reinforcement Learning, Deployment efficiency, Reward free RL, Low adaptive RL
TL;DR: We design algorithms for reward free RL under linear MDP with near-optimal deployment complexity and sample complexity.
Abstract: We study the problem of deployment efficient reinforcement learning (RL) with linear function approximation under the \emph{reward-free} exploration setting. This is a well-motivated problem because deploying new policies is costly in real-life RL applications. Under the linear MDP setting with feature dimension $d$ and planning horizon $H$, we propose a new algorithm that collects at most $\widetilde{O}(\frac{d^2H^5}{\epsilon^2})$ trajectories within $H$ deployments to identify $\epsilon$-optimal policy for any (possibly data-dependent) choice of reward functions. To the best of our knowledge, our approach is the first to achieve optimal deployment complexity and optimal $d$ dependence in sample complexity at the same time, even if the reward is known ahead of time. Our novel techniques include an exploration-preserving policy discretization and a generalized G-optimal experiment design, which could be of independent interest. Lastly, we analyze the related problem of regret minimization in low-adaptive RL and provide information-theoretic lower bounds for switching cost and batch complexity.
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