Provably Good Batch Reinforcement Learning Without Great Exploration

Understand

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.