@inproceedings{Venugopal2024MABL,
author = {Aravind Venugopal and Stephanie Milani and Fei Fang and Balaraman Ravindran},
title = {MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
year = {2024},
keywords = {multi-agent reinforcement learning, world models, sample efficiency, latent variable models},
abstract = {We design a bi-level latent-variable world model for multi-agent reinforcement learning that encodes global information during training while supporting fully decentralized execution, outperforming prior multi-agent world models in sample efficiency across SMAC, Flatland, and MAMuJoCo.}
}