Decentralized Bandit Learning of Markov Perfect Equilibria
We study decentralized bandit learning in discounted infinite-horizon, general-sum Markov games with continuous actions. Agents observe the public state, but only their own realized rewards, and use simultaneous perturbation stochastic approximation to update their actions. Under strong monotonicity of the negative run...
Felix Höfer, H. Soner
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