A system and method of tracking an object and navigating an object tracking robot includes receiving tracking sensor input representing the object and an environment at multiple times, responsive to the tracking sensor input, calculating positions of the robot and the object at the multiple times, and using a computer implemented deep reinforcement learning (DRL) network trained as a function of tracking quality rewards and robot navigation path quality rewards, the DRL network being responsive to the calculated positions of the robot and the object at the multiple times to determine possible actions specifying movement of the object tracking robot from a current position of the robot and target, determine quality values (Q-values) for the possible actions, and select an action as a function of the Q-values. A method of training the DRL network is also included.