Reinforcement Learning for Autonomous Vehicles

Abstract—Reinforcement Learning (RL) has emerged as a
transformative technology for autonomous vehicles, enabling
sophisticated decision-making systems that enhance driving safety,efficiency, & adaptability. This paper explores the application of RL algorithms in the context of autonomous vehicle control,focusing on the development of intelligent agents capable of learning optimal driving policies through interaction with com- plex environments. The study reviews various RL techniques, including Deep Q-Learning, Policy Gradient methods, & Actor- Critic frameworks, evaluating their effectiveness in addressing key challenges such as real-time decision making, dynamic environment adaptation, & multi-agen t interactions. Emphasis is placed on the design of reward functions, exploration strategies, &
simulation environments, which are crucial for training robust & reliable autonomous driving systems. Case studies demonstrate the application of RL in scenarios such as lane- keeping, adaptive cruise control, & collision avoidance. The paper also discusses advancements in simulation platforms & hardware acceleration that facilitate scalable RL experiments. By integrating theoretical insights with practical implementa- tions, this work provides a
comprehensive overview of current developments in RL for
autonomous vehicles & identifies future research directions aimed at overcoming limitations & achieving safer, more efficient.