  To improve efficiency and reduce failures in autonomous vehicles, research
has focused on developing robust and safe learning methods that take into
account disturbances in the environment. Existing literature in robust
reinforcement learning poses the learning problem as a two player game between
the autonomous system and disturbances. This paper examines two different
algorithms to solve the game, Robust Adversarial Reinforcement Learning and
Neural Fictitious Self Play, and compares performance on an autonomous driving
scenario. We extend the game formulation to a semi-competitive setting and
demonstrate that the resulting adversary better captures meaningful
disturbances that lead to better overall performance. The resulting robust
policy exhibits improved driving efficiency while effectively reducing
collision rates compared to baseline control policies produced by traditional
reinforcement learning methods.

Machine Learning;Robotics;Machine Learning