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Multi-robot systems can benefit from reinforcement learning (RL) algorithmsthat learn behaviours in a small number of trials, a property known as sampleefficiency. This research thus investigates the use of learned world models toimprove sample efficiency. We present a novel multi-agent model-based RLalgorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing theCentralized Learning for Decentralized Execution (CLDE) framework. CLDEalgorithms allow a group of agents to act in a fully decentralized manner aftertraining. This is a desirable property for many systems comprising of multiplerobots. MAMBPO uses a learned world model to improve sample efficiency comparedto model-free Multi-Agent Soft Actor-Critic (MASAC). We demonstrate this on twosimulated multi-robot tasks, where MAMBPO achieves a similar performance toMASAC, but requires far fewer samples to do so. Through this, we take animportant step towards making real-life learning for multi-robot systemspossible.

MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models