  Cognitive radars are systems that rely on learning through interactions of
the radar with the surrounding environment. To realize this, radar transmit
parameters can be adapted such that they facilitate some downstream task. This
paper proposes the use of deep reinforcement learning (RL) to learn policies
for gain control under the object detection task. The YOLOv3 single-shot object
detector is used for the downstream task and will be concurrently used
alongside the RL agent. Furthermore, a synthetic dataset is introduced which
models the radar environment with use of the Grand Theft Auto V game engine.
This approach allows for simulation of vast amounts of data with flexible
assignment of the radar parameters to aid in the active learning process.
