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Introduction

Deep reinforcement learning has gained significant attention in recent years due to its ability to learn complex patterns and make decisions in various environments. In the field of game theory, deep reinforcement learning has shown promising results in solving games with imperfect information, multi-agent interactions, and strategic decision-making processes. This thesis explores the application of deep reinforcement learning in game theory to address challenges and optimize decision-making strategies.

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
2.1 Overview of deep reinforcement learning
2.2 Applications of deep reinforcement learning in game theory
2.3 Game theory fundamentals
2.4 Traditional approaches in game theory
2.5 Deep reinforcement learning algorithms
2.6 Multi-agent reinforcement learning
2.7 Imperfect information games
2.8 Markov decision process
2.9 Policy gradient methods
2.10 Deep Q-learning

Chapter Three: System Design and Methodology
3.1 Problem formulation
3.2 Data preprocessing
3.3 Environment design
3.4 Algorithm selection
3.5 Model architecture
3.6 Training process
3.7 Evaluation metrics
3.8 Hyperparameter tuning

Chapter Four: System Implementation
4.1 Environment setup
4.2 Data collection and preprocessing
4.3 Model training
4.4 Experimentation
4.5 Performance analysis
4.6 Results interpretation
4.7 Comparison with traditional approaches
4.8 Challenges and solutions

Chapter Five: 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

Thesis Overview:

Deep reinforcement learning has emerged as a powerful technique for solving complex decision-making problems in various domains, including game theory. This thesis focuses on the application of deep reinforcement learning in game theory to address challenges such as imperfect information, multi-agent interactions, and strategic decision-making processes. The thesis includes a comprehensive literature review, system design, methodology, implementation, and conclusion.

The literature review covers the fundamentals of deep reinforcement learning, game theory, traditional approaches in game theory, and deep reinforcement learning algorithms. The system design and methodology section discuss problem formulation, data preprocessing, environment design, algorithm selection, model architecture, training process, evaluation metrics, and hyperparameter tuning. The system implementation chapter details the environment setup, data collection, preprocessing, model training, experimentation, performance analysis, results interpretation, and comparison with traditional approaches.

In conclusion, this thesis provides insights into the application of deep reinforcement learning in game theory and its potential for optimizing decision-making strategies in complex environments. The findings contribute to the existing literature and offer implications for future research in the field.

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