When Is a Game a Markov Decision Process? Correspondences Between Game Theory and Reinforcement Learning
Read the original on arXiv Machine Learning →The paper establishes formal theorems linking game theory and reinforcement learning, showing that a game can be viewed as a Markov decision process in three distinct ways—definitional identity, exact reduction, or structural analogy. It systematically maps every standard game class and solution concept to corresponding reinforcement learning models, training procedures, and complexity results. The work also identifies where these correspondences break down, such as non‑stationarity, impossibility of uncoupled convergence to Nash equilibrium, and gaps between coarse correlated and correlated equilibria.
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