Opponent learning for multi-agent system simulation

Multi-agent reinforcement learning is a challenging issue in artificial intelligence researches. In this paper, the reinforcement learning model and algorithm in multi-agent system simulation context are brought forward. We suggest and validate an opponent modeling learning to the problem of finding good policies for agents accommodated in an adversarial artificial world. The feature of the algorithm exhibits in that when in a multi-player adversarial environment the immediate reward depends on not only agent's action choose but also its opponent's trends. Experiment results show that the learning agent finds optimal policies in accordance with the reward functions provided.