DTDE: A new cooperative multi-agent reinforcement learning framework

Abstract

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.