learning in the time-dependent minority game
We study learning in the time-dependent Minority Game (MG). The MG is a repeated conflicting interest game involving a large number of agents. So far, the learning mechanisms studied were rather naive and involved only exploitation of the best strategy so far at the expense of exploring new strategies. Instead, we use a reinforcement learning method called Q-learning and show how it improves the results on MG extensions of increasing difficulty.
minority games reinforcement learning multi-agent