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Continuous-Time Reinforcement Learning for $N$-Player Stochastic Differential Games with Exploratory Policies
We study entropy-regularized exploratory control in finite $N$-player stochastic differential games under a response model in which each player conditions on the opponents' currently realized actions and evaluates continuation with that profile frozen. The resulting Gibbs best responses form a syste
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