Deep Reinforcement Learning in Autonomous Vehicles: An Indian Perspective
DOI:
https://doi.org/10.36676/urr.v8.i4.1405Keywords:
Deep Reinforcement Learning, Autonomous Vehicles, Indian TrafficAbstract
This paper explores the application of Deep Reinforcement Learning (DRL) in developing autonomous vehicles within the Indian context. India presents a unique challenge for autonomous systems due to its complex traffic conditions, varied road infrastructures, and lack of standardized traffic rules. The study analyzes how DRL can be utilized to train autonomous vehicles to navigate these environments effectively. Techniques such as Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) are discussed with case studies on simulation environments. The research further examines the potential economic and societal impacts of autonomous vehicles in India, focusing on scalability, affordability, and adaptation to local conditions
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