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.
In practice, replacing the optimal vector field with a parametric neural approximation vθfine(xt, t|s) introduces an error e(xt, t) =v θfine(xt, t|s)−v ∗,fine(xt, t|s)
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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.