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Multi-agent RL is rendered difficult due to the non-stationary nature ofenvironment perceived by individual agents. Theoretically sound methods usingthe REINFORCE estimator are impeded by its high-variance, whereasvalue-function based methods are affected by issues stemming from their ad-hochandling of situations like inter-agent communication. Methods like MADDPG arefurther constrained due to their requirement of centralized critics etc. Inorder to address these issues, we present MA-Dreamer, a model-based method thatuses both agent-centric and global differentiable models of the environment inorder to train decentralized agents' policies and critics using model-rolloutsa.k.a `imagination'. Since only the model-training is done off-policy,inter-agent communication/coordination and `language emergence' can be handledin a straight-forward manner. We compare the performance of MA-Dreamer withother methods on two soccer-based games. Our experiments show that in long-termspeaker-listener tasks and in cooperative games with strongpartial-observability, MA-Dreamer finds a solution that makes effective use ofcoordination, whereas competing methods obtain marginal scores and failoutright, respectively. By effectively achieving coordination and communicationunder more relaxed and general conditions, out method opens the door to thestudy of more complex problems and population-based training.

MA-Dreamer: Coordination and communication through shared imagination