  Fast adapting to unknown peers (partners or opponents) with different
strategies is a key challenge in multi-agent games. To do so, it is crucial for
the agent to efficiently probe and identify the peer's strategy, as this is the
prerequisite for carrying out the best response in adaptation. However, it is
difficult to explore the strategies of unknown peers, especially when the games
are partially observable and have a long horizon. In this paper, we propose a
peer identification reward, which rewards the learning agent based on how well
it can identify the behavior pattern of the peer over the historical context,
such as the observation over multiple episodes. This reward motivates the agent
to learn a context-aware policy for effective exploration and fast adaptation,
i.e., to actively seek and collect informative feedback from peers when
uncertain about their policies and to exploit the context to perform the best
response when confident. We evaluate our method on diverse testbeds that
involve competitive (Kuhn Poker), cooperative (PO-Overcooked), or mixed
(Predator-Prey-W) games with peer agents. We demonstrate that our method
induces more active exploration behavior, achieving faster adaptation and
better outcomes than existing methods.
