Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

Iou-Jen Liu; Unnat Jain; Raymond A. Yeh; Alexander G. Schwing

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arXiv · 2107.11444

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

Iou-Jen Liu·Unnat Jain·Raymond A. Yeh·Alexander G. Schwing

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Abstract

Exploration is critical for good results in deep reinforcement learning and has attracted much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. Very recently, exploration methods that consider cooperation among multiple agents have been developed. However, existing methods suffer from a common challenge: agents struggle to identify states that are worth exploring, and hardly coordinate exploration efforts toward those states. To address this shortcoming, in this paper, we propose cooperative multi-agent exploration (CMAE): agents share a common goal while exploring. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. Then, agents are trained to reach this goal in a coordinated manner. We demonstrate that CMAE consistently outperforms baselines on various tasks, including a sparse-reward version of the multiple-particle environment (MPE) and the Starcraft multi-agent challenge (SMAC).

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BibTeX RIS

Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, Alexander G. Schwing. 2021-07-23. Cooperative Exploration for Multi-Agent Deep Reinforcement Learning. https://arxiv.org/abs/2107.11444

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