Deep Reinforcement Learning for Autonomous Vehicles

This study investigates the application of Deep Reinforcement Learning (DRL) in enhancing the performance and decision-making capabilities of Autonomous Vehicles (AVs). By leveraging DRL techniques such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Asynchronous Advantage Actor-Critic (A3C), AVs can learn to make intelligent, real-time decisions in dynamic environments. The paper uses a comparative analysis of these DRL algorithms to evaluate their effectiveness in path planning, lane-keeping, and obstacle avoidance tasks. Key challenges encountered in deploying DRL in AVs include scalability, safety, real-time decision-making, and the sim-to-real transfer of models. The study also highlights the role of simulation platforms like CARLA and OpenAI Gym in training DRL models and discusses their impact on model reliability and real-world performance. The findings suggest that while DRL shows great promise for improving AV capabilities, challenges remain in its practical application, particularly regarding the safety and ethical implications of decision-making in critical driving situations. Future research directions include enhancing simulation environments, reward function design, and developing robust safety protocols to ensure the safe deployment of DRL-powered AVs in real-world scenarios.