stochastic game
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Policy optimization, i.e. algorithms that learn to make sequential decisions by local search on the agent's policy directly, is a widely used class of algorithms in reinforcement learning [40, 44, 45].
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A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.
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Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
One such example is two-player zero-sum Markov games, in which efficient ways to compute a Nash equilibrium are known. Inspired by zero-sum polymatrix normal-form games (Cai et al., 2016), we define a class of zero-sum
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Learning Mean-Field Games
Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang
Neural Information Processing Systems http://nips.cc/
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