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Introduction

Autonomous racing has gained significant popularity in recent years with the development of advanced technologies such as deep reinforcement learning (DRL). DRL has shown promise in training autonomous agents to make decisions in complex and dynamic environments, making it a suitable candidate for autonomous racing applications. In this thesis, we explore the use of DRL for autonomous racing and investigate its potential to improve the performance and efficiency of autonomous racing vehicles.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 History of autonomous racing
2.2 Deep reinforcement learning
2.3 Applications of DRL in autonomous driving
2.4 Challenges in autonomous racing
2.5 Previous studies on DRL for autonomous racing
2.6 Comparison with other approaches
2.7 State-of-the-art techniques in autonomous racing
2.8 Advantages and disadvantages of DRL in autonomous racing
2.9 Future research directions
2.10 Conclusion

Chapter 3: System Design and Methodology
3.1 Problem formulation
3.2 Data collection and preprocessing
3.3 Agent architecture
3.4 Reward function design
3.5 Exploration vs. exploitation strategies
3.6 Training algorithm
3.7 Evaluation metrics
3.8 Hyperparameter tuning
3.9 Simulation environment
3.10 Conclusion

Chapter 4: System Implementation
4.1 Software and hardware requirements
4.2 Implementation of the agent
4.3 Training process
4.4 Testing and validation
4.5 Fine-tuning
4.6 Performance analysis
4.7 Results visualization
4.8 Challenges and solutions
4.9 Code optimization
4.10 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for future research
5.4 Limitations of the study
5.5 Conclusion and final remarks

Thesis Overview:

Deep reinforcement learning (DRL) has emerged as a promising approach for training autonomous agents to perform tasks in complex and dynamic environments. In the context of autonomous racing, DRL offers a unique opportunity to develop high-performance racing algorithms that can navigate tracks efficiently and competitively. This thesis explores the application of DRL in autonomous racing and investigates its potential to revolutionize the field.

The thesis begins with a comprehensive introduction that outlines the background, problem statement, objectives, limitations, scope, significance, structure, and definition of key terms related to the study. Following this, a thorough literature review is conducted to examine the history of autonomous racing, the principles of DRL, previous research in the field, and the challenges and opportunities in applying DRL to autonomous racing.

The thesis then delves into the system design and methodology section, where the problem formulation, data collection, agent architecture, reward function design, training algorithm, evaluation metrics, and simulation environment are outlined. This chapter provides a detailed overview of how the autonomous racing system was designed and implemented using DRL techniques.

Next, the system implementation chapter discusses the software and hardware requirements, the implementation of the agent, the training process, testing, validation, fine-tuning, performance analysis, results visualization, challenges, and solutions, and code optimization. This chapter provides insights into the practical aspects of implementing a DRL-based autonomous racing system.

Finally, the conclusion and summary chapter offers a recap of the findings, contributions to the field, implications for future research, limitations of the study, and final remarks. Overall, this thesis contributes to the growing body of research on DRL for autonomous racing and highlights the potential of this technology to enhance the performance and efficiency of autonomous racing vehicles.

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