Sobolev regularization on the witness function enables global convergence of MMD gradient flows for both sampling and generative modeling without isoperimetric assumptions.
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ATD(λ) adapts TD(λ) in MARL via a density ratio estimator on past/current replay buffers to assign λ per state-action pair, yielding competitive or better results than fixed-λ QMIX and MAPPO on SMAC and Gfootball.
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Sobolev Regularized MMD Gradient Flow
Sobolev regularization on the witness function enables global convergence of MMD gradient flows for both sampling and generative modeling without isoperimetric assumptions.
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Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning
ATD(λ) adapts TD(λ) in MARL via a density ratio estimator on past/current replay buffers to assign λ per state-action pair, yielding competitive or better results than fixed-λ QMIX and MAPPO on SMAC and Gfootball.