  Ad hoc teamwork problem describes situations where an agent has to cooperate
with previously unseen agents to achieve a common goal. For an agent to be
successful in these scenarios, it has to have a suitable cooperative skill. One
could implement cooperative skills into an agent by using domain knowledge to
design the agent's behavior. However, in complex domains, domain knowledge
might not be available. Therefore, it is worthwhile to explore how to directly
learn cooperative skills from data. In this work, we apply meta-reinforcement
learning (meta-RL) formulation in the context of the ad hoc teamwork problem.
Our empirical results show that such a method could produce robust cooperative
agents in two cooperative environments with different cooperative
circumstances: social compliance and language interpretation. (This is a full
paper of the extended abstract version.)
