  Multi-robot systems can benefit from reinforcement learning (RL) algorithms
that learn behaviours in a small number of trials, a property known as sample
efficiency. This research thus investigates the use of learned world models to
improve sample efficiency. We present a novel multi-agent model-based RL
algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing the
Centralized Learning for Decentralized Execution (CLDE) framework. CLDE
algorithms allow a group of agents to act in a fully decentralized manner after
training. This is a desirable property for many systems comprising of multiple
robots. MAMBPO uses a learned world model to improve sample efficiency compared
to model-free Multi-Agent Soft Actor-Critic (MASAC). We demonstrate this on two
simulated multi-robot tasks, where MAMBPO achieves a similar performance to
MASAC, but requires far fewer samples to do so. Through this, we take an
important step towards making real-life learning for multi-robot systems
possible.
