Reinforcement learning with restrictions on the action set

Mario Bravo, Mathieu Faure

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Consider a two-player normal-form game repeated over time. We introduce an adaptive learning procedure, where the players only observe their own realized payoff at each stage. We assume that agents do not know their own payoff function and have no information on the other player. Furthermore, we assume that they have restrictions on their own actions such that, at each stage, their choice is limited to a subset of their action set. We prove that the empirical distributions of play converge to the set of Nash equilibria for zero-sum and potential games, and games where one player has two actions.

Original languageEnglish
Pages (from-to)287-312
Number of pages26
JournalSIAM Journal on Control and Optimization
Issue number1
StatePublished - 2015
Externally publishedYes


  • Best-response dynamics
  • Learning in games
  • Reinforcement learning
  • Stochastic approximations


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