Urban Intersection Simulation and Verification via Deep Reinforcement Learning Algorithms

Abstract

Reinforcement Learning (RL) uses rewards to have iteration and update the next state for training in an unknown and complex environment. This paper aims to find a possible solution for the traffic congestion problem and train four Deep Reinforcement Learning (DRL) algorithms to verify the urban intersection simulation environment in the different discussed dimensions, including practicability, efficiency, safety, complexity, and limitation. The experiment result shows that the four DRL algorithms are efficient in the RL intersection simulation. This paper has succeeded in verifying this RL environment in the comparison and expands the experiment with three conclusions: The agent can train by Deep Q-network(DQN), DoubleDQN, DuelingNet DQN, and Categorical DQN algorithms to be practical and efficient. As the experiment results show, DuelingNet takes less time to finish the training, and Categorical DQN has reduced the collision rate after a while. However, the RL simulation environment lacks complexity, causing limitations in solving more complex problems, including the lack of simulation of pedestrian behaviors and the prediction of emergency events. This paper recommends creating a more complex urban intersection simulation that includes exceptional cases for the RL agent environment and more traffic pressure for the intersection to improve the faster and safer response in future automatic driving.