Doubly Optimal Policy Evaluation for Reinforcement Learning

Published: 22 Jan 2025, Last Modified: 01 Mar 2025ICLR 2025 PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Reinforcement Learning
TL;DR: Our method substantially improves the efficiency of policy evaluation in reinforcement learning.
Abstract:

Policy evaluation estimates the performance of a policy by (1) collecting data from the environment and (2) processing raw data into a meaningful estimate. Due to the sequential nature of reinforcement learning, any improper data-collecting policy or data-processing method substantially deteriorates the variance of evaluation results over long time steps. Thus, policy evaluation often suffers from large variance and requires massive data to achieve the desired accuracy. In this work, we design an optimal combination of data-collecting policy and data-processing baseline. Theoretically, we prove our doubly optimal policy evaluation method is unbiased and guaranteed to have lower variance than previously best-performing methods. Empirically, compared with previous works, we show our method reduces variance substantially and achieves superior empirical performance.

Primary Area: reinforcement learning
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Submission Number: 7995
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