CoDreamer: Communication-Based Decentralised World Models
<|reference_start|>CoDreamer: Communication-Based Decentralised World Models: 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.<|reference_end|>
@article{toledo2024codreamer:,
    title={CoDreamer: Communication-Based Decentralised World Models},
    author={Edan Toledo, Amanda Prorok},
    journal={arXiv preprint arXiv:2406.13600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13600},
    primaryClass={cs.AI}
}