Heterogeneous multi-Agent reinforcement learning algorithm integrating Prior-knowledge

In recent years, the breakthrough of machine learning based on deep reinforcement learning provides a new development direction for intelligent game confrontation. In order to solve the problems of slow convergence speed and great difference in training effect of heterogeneous multi-agent reinforcement learning algorithm in intelligent confrontation, this paper proposes a priori knowledge-driven multi-agent reinforcement learning game antagonism algorithm PK-MADDPG, and constructs a MADDPG model under the framework of double Critic. The model uses the experience first replay technique to optimize the prior knowledge extraction, thus achieving remarkable results in the training of game confrontation. In the national competition of MaCA heterogeneous multi-agent game confrontation, the paper compares the game confrontation results of PK-MADDPG algorithm with classical rule algorithm, and verifies the effectiveness of the algorithm proposed in this paper.