Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Temporal-Causal Inference for Reinforcement Learning (TCIRL), a framework that jointly learns a control policy and infers the hidden temporal cause of the phase transition and recovers the correct cause DFA and matches the full-information baseline in both domains.
Jan Corazza, Daniil Kaminskyi, Simon Lutz et al.
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