Provably Good Batch Reinforcement Learning Without Great Exploration
This addresses the problem of reliable policy learning from fixed datasets for high-stakes applications, offering a more robust solution compared to prior methods that rely on restrictive assumptions.
The paper tackles the challenge of batch reinforcement learning where new policies may perform poorly due to over-optimism from limited data, by proposing a conservative update to Bellman backups that provides stronger theoretical guarantees without requiring strong concentrability assumptions. The result is an algorithm that can find approximately optimal policies within the explored state-action space, as demonstrated empirically against state-of-the-art baselines.
Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Doing batch RL in a way that yields a reliable new policy in large domains is challenging: a new decision policy may visit states and actions outside the support of the batch data, and function approximation and optimization with limited samples can further increase the potential of learning policies with overly optimistic estimates of their future performance. Recent algorithms have shown promise but can still be overly optimistic in their expected outcomes. Theoretical work that provides strong guarantees on the performance of the output policy relies on a strong concentrability assumption, that makes it unsuitable for cases where the ratio between state-action distributions of behavior policy and some candidate policies is large. This is because in the traditional analysis, the error bound scales up with this ratio. We show that a small modification to Bellman optimality and evaluation back-up to take a more conservative update can have much stronger guarantees. In certain settings, they can find the approximately best policy within the state-action space explored by the batch data, without requiring a priori assumptions of concentrability. We highlight the necessity of our conservative update and the limitations of previous algorithms and analyses by illustrative MDP examples, and demonstrate an empirical comparison of our algorithm and other state-of-the-art batch RL baselines in standard benchmarks.