GCReinSL adds Q-conditioned maximization to supervised offline RL, using normalizing flows to estimate goal-reaching probabilities and expectile regression to condition actions on the best in-distribution value, improving trajectory stitching.
Accelerating goal-conditioned reinforcement learning algorithms and research
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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization
GCReinSL adds Q-conditioned maximization to supervised offline RL, using normalizing flows to estimate goal-reaching probabilities and expectile regression to condition actions on the best in-distribution value, improving trajectory stitching.