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Introduction

Deep reinforcement learning (DRL) has emerged as a powerful tool for training autonomous agents to make intelligent decisions in complex environments. Autonomous underwater vehicles (AUVs) are unmanned vehicles capable of operating underwater without direct human control. The combination of DRL and AUVs has the potential to revolutionize underwater exploration, surveillance, and marine research. This thesis explores the application of DRL techniques to train AUVs for various underwater tasks.

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 Two: Literature Review
– Overview of DRL techniques
– Applications of DRL in robotics
– Previous studies on DRL for AUVs
– Challenges and limitations of DRL for AUVs
– Comparison of DRL algorithms for AUVs
– Case studies of DRL implemented in AUVs
– Future trends in DRL for AUVs
– Ethical considerations in using DRL for AUVs

Chapter Three: System Design and Methodology
– Selection of AUV platform
– Sensor integration for DRL
– Design of reward function
– Environment simulation
– Training process for DRL
– Evaluation metrics
– Parameter tuning
– Data collection and preprocessing

Chapter Four: System Implementation
– Hardware setup
– Software implementation
– Training the AUV model
– Testing and evaluation
– Performance analysis
– Improvements and optimizations
– Deployment considerations

Chapter Five: Conclusion and Summary
– Summary of findings
– Contributions of the study
– Future research directions
– Conclusion

Thesis Overview

Deep reinforcement learning (DRL) has shown great potential in training autonomous underwater vehicles (AUVs) to navigate and perform tasks in underwater environments. This thesis aims to explore the application of DRL techniques for enhancing the autonomy and intelligence of AUVs. The study begins with an introduction to the background, scope, and significance of the research, followed by a literature review that examines previous studies on DRL for AUVs and identifies gaps in the existing research.

The system design and methodology chapter will detail the process of selecting an AUV platform, integrating sensors for DRL, designing a reward function, creating an environment simulation, training the AUV model, and evaluating its performance. The implementation chapter will describe the hardware and software setup, training process, testing, performance analysis, and potential improvements for the AUV model.

In the conclusion and summary chapter, the findings of the study will be summarized, the contributions of the research will be highlighted, and future research directions will be discussed. Overall, this thesis aims to advance the field of DRL for AUVs and contribute to the development of intelligent autonomous underwater vehicles for various applications.

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