Multiagent Adversarial Collaborative Learning via Mean-Field Theory

Multiagent reinforcement learning (MARL) has recently attracted considerable attention from both academics and practitioners. Core issues, e.g., the curse of dimensionality due to the exponential growth of agent interactions and nonstationary environments due to simultaneous learning, hinder the large-scale proliferation of MARL. These problems deteriorate with an increased number of agents. To address these challenges, we propose an adversarial collaborative learning method in a mixed cooperative-competitive environment, exploiting friend-or-foe Q-learning and mean-field theory. We first treat neighbors of agent i as two coalitions ( i 's friend and opponent coalition, respectively), and convert the Markov game into a two-player zero-sum game with an extended action set. By exploiting mean-field theory, this new game simplifies the interactions as those between a single agent and the mean effects of friends and opponents. A neural network is employed to learn the optimal mean effects of these two coalitions, which are trained via adversarial max and min steps. In the max step, with fixed policies of opponents, we optimize the friends' mean action to maximize their rewards. In the min step, the mean action of opponents is trained to minimize the friends' rewards when the policies of friends are frozen. These two steps are proved to converge to a Nash equilibrium. Then, another neural network is applied to learn the best response of each agent toward the mean effects. Finally, the adversarial max and min steps can jointly optimize the two networks. Experiments on two platforms demonstrate the learning effectiveness and strength of our approach, especially with many agents.