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

• URL: http://arxiv.org/abs/2103.03662v1
• Date: Fri, 5 Mar 2021 13:37:23 GMT
• Title: MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models
• Authors: Dani\"el Willemsen, Mario Coppola and Guido C.H.E. de Croon
• Abstract summary: Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials. We present a novel multi-agent model-based RL algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO)
• Score: 4.84279798426797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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