  A key goal of ad hoc teamwork is to develop a learning agent that cooperates
with unknown teams, without resorting to any pre-coordination protocol. Despite
a vast number of ad hoc teamwork algorithms in the literature, most of them
cannot address the problem of learning to cooperate with a completely unknown
team, unless it learns from scratch. This article presents a novel approach
that uses transfer learning alongside the state-of-the-art PLASTIC-Policy to
adapt to completely unknown teammates quickly. We test our solution within the
Half Field Offense simulator with five different teammates. The teammates were
designed independently by developers from different countries and at different
times. Our empirical evaluation shows that it is advantageous for an ad hoc
agent to leverage its past knowledge when adapting to a new team instead of
learning how to cooperate with it from scratch.
