Methods › Reinforcement Learning › State Similarity Metrics › Policy Similarity Metric
Policy Similarity Metric
Introduced by Rishabh Agarwal et al. in Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Policy Similarity Metric, or PSM, is a similarity metric for measuring behavioral similarity between states in reinforcement learning. It assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. PSM is reward-agnostic, making it more robust for generalization compared to approaches that rely on reward information.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained Model 3 Mar 2025 · 1 repository
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Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning 13 Jan 2021 · 1 repository · arXiv:2101.05265
Tasks archive 2025-07-28
11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections