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To improve efficiency and reduce failures in autonomous vehicles, researchhas focused on developing robust and safe learning methods that take intoaccount disturbances in the environment. Existing literature in robustreinforcement learning poses the learning problem as a two player game betweenthe autonomous system and disturbances. This paper examines two differentalgorithms to solve the game, Robust Adversarial Reinforcement Learning andNeural Fictitious Self Play, and compares performance on an autonomous drivingscenario. We extend the game formulation to a semi-competitive setting anddemonstrate that the resulting adversary better captures meaningfuldisturbances that lead to better overall performance. The resulting robustpolicy exhibits improved driving efficiency while effectively reducingcollision rates compared to baseline control policies produced by traditionalreinforcement learning methods.

Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning