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Introduction:

Reinforcement learning is an area of Machine Learning where an agent learns to make decisions by interacting with an environment and receiving rewards for its actions. This type of learning has shown promising results in various applications, including autonomous vehicles. Autonomous vehicles are vehicles that can operate without human intervention, using sensors and algorithms to perceive the environment and make decisions. In recent years, researchers have been exploring the application of reinforcement learning techniques in autonomous vehicles to improve their decision-making capabilities and enhance their performance.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Research Problem
1.3 Objective of Study
1.4 Limitations of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Reinforcement Learning
2.2 Reinforcement Learning in Autonomous Vehicles
2.3 Current Trends and Challenges in Autonomous Vehicles
2.4 Existing Research on Reinforcement Learning for Autonomous Vehicles

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Experimental Design
3.3 Implementation of Reinforcement Learning Algorithms
3.4 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Analysis of Experimental Results
4.2 Comparison with Existing Methods
4.3 Impact of Reinforcement Learning on Autonomous Vehicles
4.4 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Conclusion

Thesis Overview:

Reinforcement learning has emerged as a promising method for enhancing the decision-making capabilities of autonomous vehicles. In this thesis, we aim to investigate the application of reinforcement learning techniques in autonomous vehicles and evaluate their effectiveness in improving performance. We will begin with an introduction to the background of the research problem, followed by a literature review on the current trends and challenges in the field of autonomous vehicles and reinforcement learning. The research methodology will outline the data collection process, experimental design, implementation of reinforcement learning algorithms, and evaluation metrics used in the study. The discussion of findings will analyze the experimental results, compare them with existing methods, and discuss the impact of reinforcement learning on autonomous vehicles. Finally, the conclusion and summary will provide a summary of key findings, contributions of the study, implications for future research, and a conclusion on the effectiveness of reinforcement learning for autonomous vehicles.

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