| Summary: | One of the challenges in multi-agent systems comes from the environmental non-stationarity that policies of all agents are evolving individually over time. Many existing multi-agent reinforcement learning (MARL) methods have been proposed to address this problem. However, these methods rely on a large amount of training data and some of them require agents to intensely communicate, which is often impractical in real-world applications. To better tackle the non-stationarity problem, this article combines model-based reinforcement learning (MBRL) and meta-learning and proposes a method called Model-based Imagined Rollouts Augmentation (MIRA). Based on an environment dynamics model, distributed agents can independently perform multi-agent rollouts with opponent models during exploitation and learn to infer the environmental non-stationarity as a latent variable using the rollouts. Based on the world model and latent-variable inference module, we perform multi-agent soft actor-critic implementation for centralized training and decentralized decision making. Empirical results on the Multi-agent Particle Environment (MPE) have proved that the algorithm has a very considerable improvement in sample efficiency as well as better convergent rewards than state-of-the-art MARL methods, including COMA, MAAC, MADDPG, and VDN.
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