Generalising Multi-Agent Cooperation through Task-Agnostic Communication
Dulhan Jayalath Steven Morad Amanda Prorok
Existing communication methods for multi-agent reinforcement learning (MARL)
in cooperative multi-robot problems are almost exclusively task-specific,
training new communication strategies for each unique task. We address this
inefficiency by introducing a communication strategy applicable to any task
within a given environment. We pre-train the communication strategy without
task-specific reward guidance in a self-supervised manner using a set
autoencoder. Our objective is to learn a fixed-size latent Markov state from a
variable number of agent observations. Under mild assumptions, we prove that
policies using our latent representations are guaranteed to converge, and upper
bound the value error introduced by our Markov state approximation. Our method
enables seamless adaptation to novel tasks without fine-tuning the
communication strategy, gracefully supports scaling to more agents than present
during training, and detects out-of-distribution events in an environment.
Empirical results on diverse MARL scenarios validate the effectiveness of our
approach, surpassing task-specific communication strategies in unseen tasks.
Our implementation of this work is available at
https://github.com/proroklab/task-agnostic-comms.