Offline Primal-Dual Reinforcement Learning for Linear MDPs
This addresses the problem of learning efficient policies from fixed datasets in continuous or large state spaces for researchers and practitioners in reinforcement learning, offering incremental improvements in sample efficiency and theoretical coverage.
The paper tackles offline reinforcement learning for infinite-horizon settings with linear MDPs, proposing a primal-dual method that achieves an ε-optimal policy with O(ε⁻⁴) sample complexity, improving on prior O(ε⁻⁵), and provides the first theoretical result for average-reward offline RL.
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from a fixed dataset of transitions collected by another policy. This problem has attracted a lot of attention recently, but most existing methods with strong theoretical guarantees are restricted to finite-horizon or tabular settings. In constrast, few algorithms for infinite-horizon settings with function approximation and minimal assumptions on the dataset are both sample and computationally efficient. Another gap in the current literature is the lack of theoretical analysis for the average-reward setting, which is more challenging than the discounted setting. In this paper, we address both of these issues by proposing a primal-dual optimization method based on the linear programming formulation of RL. Our key contribution is a new reparametrization that allows us to derive low-variance gradient estimators that can be used in a stochastic optimization scheme using only samples from the behavior policy. Our method finds an $\varepsilon$-optimal policy with $O(\varepsilon^{-4})$ samples, improving on the previous $O(\varepsilon^{-5})$, while being computationally efficient for infinite-horizon discounted and average-reward MDPs with realizable linear function approximation and partial coverage. Moreover, to the best of our knowledge, this is the first theoretical result for average-reward offline RL.