  Mixed cooperative-competitive control scenarios such as human-machine
interaction with individual goals of the interacting partners are very
challenging for reinforcement learning agents. In order to contribute towards
intuitive human-machine collaboration, we focus on problems in the continuous
state and control domain where no explicit communication is considered and the
agents do not know the others' goals or control laws but only sense their
control inputs retrospectively. Our proposed framework combines a learned
partner model based on online data with a reinforcement learning agent that is
trained in a simulated environment including the partner model. Thus, we
overcome drawbacks of independent learners and, in addition, benefit from a
reduced amount of real world data required for reinforcement learning which is
vital in the human-machine context. We finally analyze an example that
demonstrates the merits of our proposed framework which learns fast due to the
simulated environment and adapts to the continuously changing partner due to
the partner approximation.
