Is Value Learning Really the Main Bottleneck in Offline RL?

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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Offline RL Bottlenecks: It's Not Just the Value Function!

This study analyzes the bottlenecks of offline reinforcement learning algorithms. Contrary to common belief, it's not just about learning accurate value functions. The findings suggest that policy extraction methods and the policy's ability to generalize to unseen states during evaluation play equally, if not more, critical roles.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Limited Scope of Policy Extraction Analysis

The in-depth analysis of policy extraction primarily focuses on continuous-action environments, which limits the direct applicability of findings to discrete-action settings where policy representation and update mechanisms differ significantly. Further investigation in discrete-action settings is needed to ensure comprehensive understanding.

Proxy Metrics for Policy Accuracy

The study uses mean squared error (MSE) as a proxy for policy accuracy, which may not fully capture the nuances of optimality, especially in cases with multiple optimal actions or imperfect expert policies. While correlated with performance, more sophisticated metrics might be needed to refine the analysis.

Rating Explanation

This study provides valuable insights into the practical bottlenecks in Offline RL, going beyond the conventional focus on value functions. The empirical analyses are extensive, and the actionable takeaways are beneficial for both practitioners and researchers. The limitations regarding the scope of policy extraction analysis and the use of proxy metrics are acknowledged, but do not significantly detract from the overall contribution. Therefore, a rating of 4 reflects a strong and impactful study with minor limitations.

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