Haobin Jiang

Informative Policy Representations in Multi-Agent Reinforcement Learning via Joint-Action Distributions.

2021 · arXiv (Cornell University)

In multi-agent reinforcement learning, the inherent non-stationarity of the environment caused by other agents' actions posed significant difficulties for an agent to learn a good policy independently. One way to deal with non-stationarity is agent …