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Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Research Questions
1.4 Significance of Study
1.5 Objective of Study
1.6 Limitation of Study
1.7 Scope of Study

Chapter 2: Literature Review
2.1 Introduction to Reinforcement Learning
2.2 Federated Learning
2.3 Multi-Agent Systems
2.4 Previous Studies on Federated Reinforcement Learning for Multi-Agent Systems

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Model Development
3.5 Validation and Testing

Chapter 4: Discussion of Findings
4.1 Analysis of Federated Reinforcement Learning Approaches
4.2 Comparison of Results
4.3 Implications of Findings
4.4 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Conclusions
5.3 Recommendations for Future Research

Brief Overview:

The thesis on “Federated Reinforcement Learning for Multi-Agent Systems” focuses on the application of federated learning in reinforcement learning for multi-agent systems. The study aims to address the challenges of collaboration and scalability in multi-agent systems by incorporating federated learning techniques. The research methodology includes a review of existing literature on reinforcement learning, federated learning, and multi-agent systems, followed by the development and testing of a federated reinforcement learning model.

The study is significant as it provides insights into how federated learning can be used to improve the performance of multi-agent systems in various applications such as autonomous driving, robotics, and healthcare. The findings of the research will contribute to the advancements in federated reinforcement learning for multi-agent systems and provide recommendations for future research in this area.

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