Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning. (arXiv:2207.02249v1 [cs.MA])

Successful deployment of multi-agent reinforcement learning often requires
agents to adapt their behaviour. In this work, we discuss the problem of
teamwork adaptation in which a team of agents needs to adapt their policies to
solve novel tasks with limited fine-tuning. Motivated by the intuition that
agents need to be able to identify and distinguish tasks in order to adapt
their behaviour to the current task, we propose to learn multi-agent task
embeddings (MATE). These task embeddings are trained using …

arxiv learning reinforcement reinforcement learning teamwork