Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning

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 identify from what they do not; minimizing over all compatible transitions yields a worst-case occupancy that lower-bounds the true one. Causal Contrastive Reinforcement Learning (CauCRL) learns a pessimistic counterfactual transition from positive and negative offline demonstrations and trains the critic on counterfactual rollouts, substantially improving success rates over standard CRL on confounded goal-reaching tasks.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.14953 [cs.LG]

(or arXiv:2610.14953v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14953 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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