Research on the application of multi-intelligence collaborative method based on imitation learning
Wei Zhang, Haiyu Zhao, Zhenyu Li, Wenshi Wang, Yucai Dong · 2023
Deep learning empowerment enables traditional reinforcement learning methods to deal with complex problems with large state space. However, one of the disadvantages of the deep reinforcement learning method is that the trained agent has limited exploration ability, requires a large amount of computing resources, and may fall into local optimization. In this paper, imitation learning is added into the training process of agents to increase the exploration ability of multiple deep reinforcement learning agents. Experiments are carried out in the multi-agent environment of Overcook. The results show that the performance of multiple agents in cooperative learning with expert strategy is significantly improved. This suggests that using expert-assisted training can help reinforcement learning agents solve complex problems.