Coordinated Multi-Agent Imitation Learning

Understand

We study the problem of imitation learning from demonstrations of multiple coordinating agents.

• One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable.
• We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies.
• In particular, our method integrates unsupervised structure learning with conventional imitation learning.