Optimistic sequential multi-agent reinforcement learning with motivational communication

Anqi Huang, Yongli Wang, Xiaoliang Zhou, Haochen Zou, Xu Dong, Xun Che

Abstract: Highlights•We propose an optimistic Q-value to explore the potential value of individual policies.•We introduce specially designed motivational communication modules to enhance teamwork.•We show that OSSMC can achieve a competitive baseline in many challenging benchmarks.