  We simulate behaviour of two independent reinforcement learning algorithms
playing the Crawford and Sobel (1982) game of strategic information
transmission. We adopt memoryless algorithms to capture learning in a static
game where a large population interacts anonymously. We show that sender and
receiver converge to Nash equilibrium play. The level of informativeness of the
sender's cheap talk decreases as the bias increases and, at intermediate level
of the bias, it matches the level predicted by the Pareto optimal equilibrium
or by the second best one. Conclusions are robust to alternative specifications
of the learning hyperparameters and of the game.
