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Introduction:

In recent years, the use of autonomous drones has become increasingly popular in various industries such as agriculture, security, and transportation. One of the key challenges in developing autonomous drones is designing a robust control system that can adapt to changing environments and make decisions in real-time. Deep reinforcement learning, a branch of machine learning that combines deep learning techniques with reinforcement learning algorithms, has shown great promise in addressing this challenge.

This thesis explores the use of deep reinforcement learning for autonomous drones, focusing on how this technology can be applied to improve the autonomy and decision-making capabilities of drones in complex environments. By leveraging the power of deep neural networks and reinforcement learning algorithms, autonomous drones can learn to navigate through obstacles, avoid collisions, and perform tasks with minimal human intervention.

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 Overview of autonomous drones
2.2 Fundamentals of reinforcement learning
2.3 Deep learning techniques for autonomous systems
2.4 Applications of deep reinforcement learning in robotics
2.5 Challenges in implementing deep reinforcement learning for drones
2.6 Existing research on autonomous drone control systems
2.7 Comparison of traditional control methods with deep reinforcement learning
2.8 Case studies of deep reinforcement learning in autonomous drones
2.9 Future trends in deep reinforcement learning for autonomous drones
2.10 Gaps in existing literature

Chapter 3: System Design and Methodology
3.1 Overview of the proposed system
3.2 Data collection and preprocessing
3.3 Reinforcement learning algorithms for autonomous drones
3.4 Deep neural network architecture
3.5 Training process and hyperparameter tuning
3.6 Simulation environment setup
3.7 Evaluation metrics for autonomous drones
3.8 Ethical considerations in autonomous drone development

Chapter 4: System Implementation
4.1 Implementation of the deep reinforcement learning algorithm
4.2 Integration with the drone control system
4.3 Evaluation of the system performance
4.4 Fine-tuning and optimization
4.5 Hardware and software requirements
4.6 Testing and validation of the autonomous drone
4.7 Performance analysis and comparison with existing methods
4.8 Scalability and generalization of the system

Chapter 5: Conclusion and Summary
5.1 Summary of the research findings
5.2 Contributions to the field of autonomous drones
5.3 Implications for future research and development
5.4 Limitations and areas for improvement
5.5 Conclusion and recommendations for further study

This thesis aims to provide a comprehensive overview of the use of deep reinforcement learning for autonomous drones, highlighting the potential benefits and challenges of implementing this technology. By examining the current state of research in this field and proposing a novel system design, this work contributes to the advancement of autonomous drone technology and lays the foundation for future research in this exciting area.

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