ME-AM adds mirror-descent entropy maximization and a mixture behavior prior to adjoint matching in flow-based policies to mitigate popularity bias and support binding in offline RL.
Path integrals and symmetry breaking for optimal control theory.Journal of Statistical Mechanics: Theory and Experiment, 2005(11), nov
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Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning
ME-AM adds mirror-descent entropy maximization and a mixture behavior prior to adjoint matching in flow-based policies to mitigate popularity bias and support binding in offline RL.