Reinforcement Learning-based Optimized Driving Behaviour Framework for Autonomous Vehicles in Intelligent Transportation System

Reinforcement learning (RL) has emerged as a crucial method for training autonomous vehicles that can navigate complex and changing environments. However, calculating effective reward functions for RL agents in autonomous vehicles remains difficult. The reward function shall capture the desired driving behaviours while also being robust to different road conditions and traffic densities. This study proposes an innovative approach to designing a reward function for RL agents in autonomous driving. The proposed approach involves a variety of high-level and low-level actions to measure the effectiveness of an agent. The agent is evaluated on a variety of randomly generated driving scenarios, which include different traffic intensities, lane keeping, overspeeding, and collision avoidance. The outcomes show that the proposed approach is highly effective in training RL agents to drive safely and efficiently in numerous environments.