Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning

arXiv:2610.14953v1 Announce Type: cross Abstract: Contrastive reinforcement learning (CRL) learns goal-conditioned values by associating state--action pairs with the states subsequently reached, assuming that no unobserved variable jointly affects the recorded action and its outcome. When offline demonstrations come from a demonstrator with information hidden from the learner, this assumption fails and the critic absorbs spurious action--outcome associations. We formalize this setting with confounded Markov decision processes and introduce counterfactual transitions that separate what the data

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