Implementing Deep Reinforcement Learning in Autonomous Control Systems

Abstract

Developing a safe and reliable autonomous vehicle has been a significant focus in recent years. Supervised learning methods require large amounts of labelled data for training, making it expensive. The performance of these agents is limited to the data provided in training and the inability to generalize performance in different environments. In addition, some driving situations, such as near-accident scenarios, are difficult to cover in the training data. As a result, the autonomous driving agent may behave unexpectedly in safety-critical situations, making it unreliable for safe transportation. Reinforcement learning is a potential solution for these issues. This research paper explores the potential of applying deep reinforcement learning techniques to autonomous driving, with a spotlight on comparing two popular deep reinforcement learning algorithms: Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG). The study uses the CARLA simulator, which provides a realistic environment and conditions for testing autonomous driving algorithms. The study finds that DDPG outperforms DQN regarding average reward, but DQN performs better regarding collision rate.