Methods › Reinforcement Learning › Policy Gradient Methods › Fisher-BRC
Fisher-BRC
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Fisher-BRC is an actor critic algorithm for offline reinforcement learning that encourages the learned policy to stay close to the data, namely parameterizing the critic as the log-behavior-policy, which generated the offline dataset, plus a state-action value offset term, which can be learned using a neural network. Behavior regularization then corresponds to an appropriate regularizer on the offset term. A gradient penalty regularizer is used for the offset term, which is equivalent to Fisher divergence regularization, suggesting connections to the score matching and generative energy-based model literature.
Papers archive 2025-07-28
1 shown of 1, 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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Offline Reinforcement Learning with Fisher Divergence Critic Regularization 14 Mar 2021 · 2 repositories · arXiv:2103.08050
Tasks archive 2025-07-28
4 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
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