InSPO updates agents sequentially with in-sample objectives and entropy regularization, avoiding out-of-distribution joint actions and converging to a quantal response equilibrium in offline MARL.
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Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
InSPO updates agents sequentially with in-sample objectives and entropy regularization, avoiding out-of-distribution joint actions and converging to a quantal response equilibrium in offline MARL.