  A major challenge for deep reinforcement learning (DRL) agents is to
collaborate with novel partners that were not encountered by them during the
training phase. This is specifically worsened by an increased variance in
action responses when the DRL agents collaborate with human partners due to the
lack of consistency in human behaviors. Recent work have shown that training a
single agent as the best response to a diverse population of training partners
significantly increases an agent's robustness to novel partners. We further
enhance the population-based training approach by introducing a Hierarchical
Reinforcement Learning (HRL) based method for Human-AI Collaboration. Our agent
is able to learn multiple best-response policies as its low-level policy while
at the same time, it learns a high-level policy that acts as a manager which
allows the agent to dynamically switch between the low-level best-response
policies based on its current partner. We demonstrate that our method is able
to dynamically adapt to novel partners of different play styles and skill
levels in the 2-player collaborative Overcooked game environment. We also
conducted a human study in the same environment to test the effectiveness of
our method when partnering with real human subjects.
