Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
This work addresses safety and efficiency issues for autonomous vehicles, but it appears incremental as it builds on existing robust reinforcement learning frameworks.
The paper tackled the problem of improving robustness and safety in autonomous vehicle control by extending a game formulation to a semi-competitive setting, resulting in a policy that demonstrated improved driving efficiency and reduced collision rates compared to baseline methods.
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.