Towards a bounded-rationality model of multi-agent social learning in games

This paper deals with the problem of multi-agent learning of a population of players, engaged in a repeated normal-form game. Assuming boundedly-rational agents, we propose a model of social learning based on trial and error, called “social reinforcement learning”. This extension of well-known Q-learning algorithm, allows players within a population to communicate and share their experiences with each other. To illustrate the effectiveness of the proposed learning algorithm, a number of simulations on the benchmark game of “Battle of Sexes” has been carried out. Results show that supplementing communication to the classical form of Q-learning, significantly improves convergence speed towards Nash equilibrium.