MASAI: Multi-agent Summative Assessment Improvement for Unsupervised Environment Design

no code implementations • ICML Workshop URL 2021 • Yiping Wang, Michael Brandon Haworth

We qualitatively and quantitatively demonstrate that, in terms of multi-agent ($\geq$ 8 agents) navigation and steering, $\textit{Students}$ trained by our approach outperform agents using heuristic search, as well as agents trained by domain randomization.

reinforcement-learning Reinforcement Learning (RL)