• Cheripally Jitendra Kumara Prasad
• Arisela Meghana
• Toomu Sahithi
• Mrs. P. Navya

Car racing, a popular sport since the early twentieth century, has grown alongside the automotive industry, entertaining fans in prominent championships such as F1, WRC, and NASCAR. Success in these competitions’ hinges on two critical factors: engineering the most potent and durable machines and training expert pilots capable of maximizing their potential on the track. With artificial intelligence achieving significant advancements in a variety of fields in recent years, a pressing question arises: Can AI outperform human drivers in motor racing? This paper delves into this inquiry by surveying the landscape of autonomous racing agents developed through deep reinforcement learning algorithms. By analysing these algorithms, which enable agents to autonomously learn and refine their racing strategies without external guidance, this study explores the potential for AI to achieve unparalleled consistency and efficiency on the racetrack. This review opens new possibilities for future developments in autonomous racing by providing insightful knowledge on how reinforcement learning methods are applied in this groundbreaking domain.

Car Racing, Deep Reinforcement Learning, Autonomous Racing, Artificial Intelligence (AI), Self-Racing Cars, Autonomous Vehicles, Racing games, Racing Simulations.

"Evolution of Deep Reinforcement Autonomous Racing: A Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.c323-c332, March-2024, Available :http://www.jetir.org/papers/JETIR2403241.pdf

"Evolution of Deep Reinforcement Autonomous Racing: A Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppc323-c332, March-2024, Available at : http://www.jetir.org/papers/JETIR2403241.pdf