  We present a method for reproducing complex multi-character interactions for
physically simulated humanoid characters using deep reinforcement learning. Our
method learns control policies for characters that imitate not only individual
motions, but also the interactions between characters, while maintaining
balance and matching the complexity of reference data. Our approach uses a
novel reward formulation based on an interaction graph that measures distances
between pairs of interaction landmarks. This reward encourages control policies
to efficiently imitate the character's motion while preserving the spatial
relationships of the interactions in the reference motion. We evaluate our
method on a variety of activities, from simple interactions such as a high-five
greeting to more complex interactions such as gymnastic exercises, Salsa
dancing, and box carrying and throwing. This approach can be used to
``clean-up'' existing motion capture data to produce physically plausible
interactions or to retarget motion to new characters with different sizes,
kinematics or morphologies while maintaining the interactions in the original
data.
