Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning

Understand

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.