  Reinforcement learning (RL) is already widely applied to applications such as
robotics, but it is only sparsely used in sensor management. In this paper, we
apply the popular Proximal Policy Optimization (PPO) approach to a multi-agent
UAV tracking scenario. While recorded data of real scenarios can accurately
reflect the real world, the required amount of data is not always available.
Simulation data, however, is typically cheap to generate, but the utilized
target behavior is often naive and only vaguely represents the real world. In
this paper, we utilize multi-agent RL to jointly generate protagonistic and
antagonistic policies and overcome the data generation problem, as the policies
are generated on-the-fly and adapt continuously. This way, we are able to
clearly outperform baseline methods and robustly generate competitive policies.
In addition, we investigate explainable artificial intelligence (XAI) by
interpreting feature saliency and generating an easy-to-read decision tree as a
simplified policy.
