  Sample efficiency is a critical challenge in reinforcement learning.
Model-based RL has emerged as a solution, but its application has largely been
confined to single-agent scenarios. In this work, we introduce CoDreamer, an
extension of the Dreamer algorithm for multi-agent environments. CoDreamer
leverages Graph Neural Networks for a two-level communication system to tackle
challenges such as partial observability and inter-agent cooperation.
Communication is separately utilised within the learned world models and within
the learned policies of each agent to enhance modelling and task-solving. We
show that CoDreamer offers greater expressive power than a naive application of
Dreamer, and we demonstrate its superiority over baseline methods across
various multi-agent environments.
