no code implementations • 12 May 2023 • Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu, Zhi Wang, Chunlin Chen
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition.
1 code implementation • 14 Oct 2022 • Xi Chen, Tianyu Shi, Qingpeng Zhao, Yuchen Sun, Yunfei Gao, Xiangjun Wang
It provides realistic 3D environments of variable complexity, various tasks, and multiple modes of interaction, where agents can learn to perceive 3D environments, navigate and plan, compete and cooperate in a human-like manner.