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Deep Q-Learning for Nash Equilibria: Nash-DQN. (arXiv:1904.10554v2 [cs.LG] UPDATED)
Oct. 25, 2022, 1:12 a.m. | Philippe Casgrain, Brian Ning, Sebastian Jaimungal
cs.LG updates on arXiv.org arxiv.org
Model-free learning for multi-agent stochastic games is an active area of
research. Existing reinforcement learning algorithms, however, are often
restricted to zero-sum games, and are applicable only in small state-action
spaces or other simplified settings. Here, we develop a new data efficient
Deep-Q-learning methodology for model-free learning of Nash equilibria for
general-sum stochastic games. The algorithm uses a local linear-quadratic
expansion of the stochastic game, which leads to analytically solvable optimal
actions. The expansion is parametrized by deep neural networks …
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