DTDE: A new cooperative multi-agent reinforcement learning framework

An illustration of the DTDE structure

A significant body of work on reinforcement learning has been focused on the single-agent tasks where the agent aims to learn a policy that maximizes the cumulative reward in a dynamic environment.1 In the past decades, quite a few single-agent-based reinforcement learning algorithms have been developed in the literature.1 Yet, it is increasingly recognized that the single-agent-based reinforcement learning algorithms may fail to effectively handle large-scale optimization (decision) tasks with joint features.

DOI:
https://doi.org/10.1016/j.xinn.2021.100162

Citation:
Wen G., Fu J., Dai P., et al. (2021). DTDE: A new cooperative multi-agent reinforcement learning framework. The Innovation. 2(4),100162.