CoDreamer: Communication-Based Decentralised World Models

Edan Toledo; Amanda Prorok

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arXiv · 2406.13600

CoDreamer: Communication-Based Decentralised World Models

Edan Toledo·Amanda Prorok

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Abstract

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.

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Edan Toledo, Amanda Prorok. 2024-06-19. CoDreamer: Communication-Based Decentralised World Models. https://arxiv.org/abs/2406.13600

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