Q-Learning in Regularized Mean-field Games

Understand

In this paper, we introduce a regularized mean-field game and study learning of this game under an infinite-horizon discounted reward function.

• Regularization is introduced by adding a strongly concave regularization function to the one-stage reward function in the classical mean-field game model.
• We establish a value iteration based learning algorithm to this regularized mean-field game using fitted Q-learning.
• The regularization term in general makes reinforcement learning algorithm more robust to the system components.