DROL trains one-step offline RL actors via top-1 dynamic routing of dataset actions to latent candidates, enabling local improvements while preserving data support and retaining cheap inference.
DeFlow : Decoupling manifold modeling and value maximization for offline policy extraction
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Fisher Decorator refines flow policies in offline RL via a local transport map and Fisher-matrix quadratic approximation of the KL constraint, yielding controllable error near the optimum and SOTA benchmark results.
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Preserve Support, Not Correspondence: Dynamic Routing for Offline Reinforcement Learning
DROL trains one-step offline RL actors via top-1 dynamic routing of dataset actions to latent candidates, enabling local improvements while preserving data support and retaining cheap inference.
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Fisher Decorator: Refining Flow Policy via a Local Transport Map
Fisher Decorator refines flow policies in offline RL via a local transport map and Fisher-matrix quadratic approximation of the KL constraint, yielding controllable error near the optimum and SOTA benchmark results.