A Novel Approach to Autonomous Driving Using Double Deep Q-Network-Bsed Deep Reinforcement Learning

Ahmed Khlifi, Mohamed Othmani, Monji Kherallah

Deep reinforcement learning (DRL) trains agents to make decisions by learning from rewards and penalties, using trial and error. It combines reinforcement learning (RL) with deep neural networks (DNNs), enabling agents to process large datasets and learn from complex environments. DRL has achieved notable success in gaming, robotics, decision-making, etc. However, real-world applications, such as self-driving cars, face challenges due to complex state and action spaces, requiring precise control. Researchers continue to develop new algorithms to improve performance in dynamic settings. A key algorithm, Deep Q-Network (DQN), uses neural networks to approximate the Q-value function but suffers from overestimation bias, leading to suboptimal outcomes. To address this, Double Deep Q-Network (DDQN) was introduced, which decouples action selection from evaluation, thereby reducing bias and promoting more stable learning. This study evaluates the effectiveness of DQN and DDQN in autonomous driving using the CARLA simulator. The key findings emphasize DDQN’s advantages in significantly reducing overestimation bias and enhancing policy performance, making it a more robust and reliable approach for complex real-world applications like self-driving cars. The results underscore DDQN’s potential to improve decision-making accuracy and stability in dynamic environments.