We consider offline reinforcement learning, where the goal is to learn a decision making policy from logged data. Offline RL—particularly when coupled with (value) function approximation to allow for generalization in large/continuous state spaces—is becoming increasingly relevant in practice, because it avoids costly and time-consuming online data collection and is well-suited to safety-critical domains. Existing sample complexity guarantees for offline value function approximation methods typically require both (1) distributional assumptions (i.e., good coverage) and (2) representational assumptions (i.e., ability to represent some or all Q-value functions) stronger than what is required for supervised learning. However, the necessity of these conditions and the fundamental limits for offline RL are not well-understood in spite of decades of research. This led Chen and Jiang (2019) to conjecture that concentrability (the most standard notion of coverage) and realizability (the weakest representation condition) alone are not sufficient for sample-efficient offline RL. We resolve this conjecture in the positive by proving (information theoretically) that even if both concentrability and realizability are satisfied, any algorithm requires sample complexity polynomial in the size of the state space to learn a non-trivial policy.
Our results show that sample-efficient offline reinforcement learning requires either restrictive coverage conditions or representation conditions beyond what is required in classical supervised learning, and highlight a phenomenon called over-coverage which serves as a fundamental barrier for offline value function approximation methods.