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We propose a new framework of Markov $\alpha$-potential games to study Markov games.
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Learning parametric closed-loop policies for Markov potential games
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Independent policy gradient methods for competitive reinforcement learning
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Fast policy extragradient methods for competitive games with entropy regularization
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Independent natural policy gradient always converges in Markov potential games
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Global convergence of multi-agent policy gradient in Markov potential games
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On the global convergence rates of decentralized softmax gradient play in Markov potential games
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