Learning with Minimal Information in Continuous Games

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Sebastian Bervoets, Mario Bravo, Mathieu Faure
We introduce a stochastic learning process called the dampened gradient approximation process. While learning models have almost exclusively focused on finite games, in this paper we design a learning process for games with continuous action sets. It is payoff-based and thus requires from players no sophistication and no knowledge of the game. We show that despite such limited information, players will converge to Nash in large classes of games. In particular, convergence to a Nash equilibrium which is stable is guaranteed in all games with strategic complements as well as in concave games; convergence to Nash often occurs in all locally ordinal potential games; convergence to a stable Nash occurs with positive probability in all games with isolated equilibria.
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