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 an encoder-decoder
architecture optimised for reconstruction of the transition and reward
functions which uniquely identify tasks. We show that a team of agents is able
to adapt to novel tasks when provided with task embeddings. We propose three
MATE training paradigms: independent MATE, centralised MATE, and mixed MATE
which vary in the information used for the task encoding. We show that the
embeddings learned by MATE identify tasks and provide useful information which
agents leverage during adaptation to novel tasks.

本文提出了一种使用多智能体任务嵌入（MATE）解决团队合作适应性问题的新方法，使用深度学习技术将任务嵌入到低维空间中进行编码和解码，利用 MATE 训练范例建立三种任务编码模型，通过分析测试结果发现，MATE 学习的任务嵌入鲜明区分不同的任务，并能在多任务情境下产生非常好的适应性效果。