A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning

Understand

Open ad hoc teamwork is the problem of training a single agent to efficiently collaborate with an unknown group of teammates whose composition may change over time.

• A variable team composition creates challenges for the agent, such as the requirement to adapt to new team dynamics and dealing with changing state vector sizes.
• These challenges are aggravated in real-world applications in which the controlled agent only has a partial view of the environment.
• In this work, we develop a class of solutions for open ad hoc teamwork under full and partial observability.