Learning in Markov Games with Incomplete Information

Junling Hu

The Markov game (also called stochastic game (Filar and Vrieze 1997)) has been adopted as a theoretical framework for multiagent reinforcement learning (Littman 1994). In a Markov game, there are n agents, each facing a Markov decision process (MDP). All agents’ MDPs are correlated through their reward functions and the state transition function. As Markov decision process provides a theoretical framework for single-agent reinforcement learning, Markov games provide such a framework for multiagent reinforcement learning.

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