MADDPG: an efficient multi-agent reinforcement learning algorithm
Xinyu Song · 2022
Reinforcement learning has made great progress in solving single agent problems in recent years. However, the development of multi-agent reinforcement learning is much slower. Many existing algorithms perform not very well in the field of multi-agent reinforcement learning. To train a multi-agent reinforcement learning model efficiently, the MADDPG algorithm based on deep neural networks is proposed in this paper. The structure of the neural networks of MADDPG is based on the Actor-Critic framework, which contains centralized critic networks and decentralized actor networks. The result shows that the three agents in the experimental environment learn to cooperate and compete well in just 50 thousand episodes. Although the model of MADDPG algorithm has high computational complexity if the number of agents is too high, it can still perform well in a multi-agent environment.