Learning to communicate through imagination with model-based deep multi-agent reinforcement learning
The human imagination is an integral component of our intelligence. Furthermore, the core utility of our imagination is deeply coupled with communication. Language, argued to have been developed through complex interaction within growing collective societies serves as an instruction to the imagination, giving us the ability to share abstract mental representations and perform joint spatiotemporal planning. In this paper, we explore communication through imagination with multi-agent reinforcement learning. Specifically, we develop a model-based approach where agents jointly plan through recurrent communication of their respective predictions of the future. Each agent has access to a learned world model capable of producing model rollouts of future states and predicted rewards, conditioned on the actions sampled from the agent's policy. These rollouts are then encoded into messages and used to learn a communication protocol during training via differentiable message passing. We highlight the benefits of our model-based approach, compared to a set of strong baselines, by developing a set of specialised experiments using novel as well as well-known multi-agent environments.
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