Reinforcement Learning-Based Guidance of Autonomous Vehicles

Abstract

Reinforcement learning (RL) has attracted significant research efforts to guide an autonomous vehicle (AV) for a collision-free path due to its advantages in investigating interactions among multiple vehicles and dynamic environments. This study deploys a Deep Q-Network (DQN) based RL algorithm with reward shaping to control an ego AV in an environment with multiple vehicles. Specifically, the state space of the RL algorithm depends on the desired destination, the ego vehicle’s location and orientation, and the location of other vehicles in the system. The training time of the proposed RL algorithm is much shorter than most current image-based algorithms. The RL algorithm also provides an extendable framework to include a varying number of vehicles in the environment and can be easily adapted to different maps without changing the setup of the RL algorithm. Three scenarios were simulated in the Cars Learn to Act (CARLA) simulator to examine the effects of the proposed RL algorithm on guiding the ego AV interacting with multiple vehicles on straight and curvy roads. Our results showed that the ego AV could learn to reach its destination within 5000 episodes for all scenarios tested.