28 citations · 29 across the 11 of their papers we have counts for
4 papers · 1 filter
Bandit learning in concave -person games
Mario Bravo, David S. Leslie, Panayotis Mertikopoulos
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games. The bandit framework accounts for extremely low-information environment…
Learning with minimal information in continuous games
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…
Reinforcement learning with restrictions on the action set
Mario Bravo, Mathieu Faure
Consider a 2-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 ass…
An adjusted payoff-based procedure for normal form games
Mario Bravo
We study a simple adaptive model in the framework of an N -player normal form game. The model consists of a repeated game where the players only know their own action space and the…