Title: Sample-Efficient Online Distributionally Robust Reinforcement Learning via General Function Approximation
Primary area: reinforcement learning
Keywords: distributionally robust, reinforcement learning, function approximation

Abstract: The deployment of reinforcement learning (RL) agents in real-world tasks is frequently hampered by performance degradation caused by mismatches between the training and target environments. Distributionally Robust RL (DR-RL) offers a principled framework to mitigate this issue by learning a policy that maximizes worst-case performance over a specified uncertainty set of transition dynamics. Despite its potential, existing DR-RL research faces two key limitations: reliance on prior knowledge of the environment -- typically access to a generative model or a large offline dataset -- and a primary focus on tabular methods that do not scale to complex problems. In this paper, we bridge these gaps by introducing an online DR-RL algorithm compatible with general function approximation. Our method learns an optimal robust policy directly from environmental interactions, eliminating the need for prior models and enabling application to complex, high-dimensional tasks. Furthermore, our theoretical analysis establishes a near-optimal sublinear regret for the algorithm under the total variation uncertainty set, demonstrating that our approach is both sample-efficient and effective.