Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
This work addresses traffic efficiency and safety for autonomous vehicles in urban environments, but it is incremental as it applies an existing method to a specific domain.
The paper tackled intersection management for autonomous vehicles by applying Trust Region Policy Optimization for fine-grained acceleration control, achieving a global design objective in a grid street plan.
Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.