Implementation of Autonomous Self-Driving Agent Using Modified Q-Learning Approach

Abstract

This research delves into a refined Q-learning-based reinforcement learning framework specifically developed to enable the training of an autonomous self-driving agent. The agent is designed to navigate complex, dynamic driving scenarios, performing critical tasks such as maintaining lanes, avoiding obstacles, and optimizing driving trajectories. These tasks are essential for real-world autonomous vehicle applications, ensuring safe and efficient operation in ever-changing environments.

To address the inherent challenges of standard Q-learning, particularly in continuous and high-dimensional state spaces, the study introduces two significant modifications: adaptive learning rates and state aggregation. Adaptive learning rates dynamically adjust the agent's learning pace, allowing for more efficient exploration and exploitation of the environment. State aggregation simplifies the state representation by grouping similar states, reducing computational complexity while maintaining decision-making accuracy. These improvements help mitigate issues such as slow convergence and suboptimal performance commonly associated with conventional Q-learning.

The study evaluates the enhanced Q-learning method using a simulated driving environment, a controlled platform that mimics real-world conditions without the risks and costs of physical testing. These simulations show that the proposed method achieves faster learning convergence, meaning the agent quickly reaches optimal behavior. Furthermore, the agent demonstrates a marked reduction in collision rates and exhibits smoother and more efficient driving paths than agents trained with standard Q-learning.

Overall, the findings underscore the potential of reinforcement learning, mainly through the integration of tailored algorithmic improvements, in advancing control systems for autonomous vehicles. This research paves the way for further developments in adaptive and efficient learning frameworks, contributing to safer, more reliable, and intelligent autonomous driving technologies.