Master Thesis: Reinforcement Learning Applications

This is my master thesis on reinforcement learning, exploring advanced techniques and applications in various domains. The work demonstrates practical implementations of cutting-edge RL algorithms and their real-world applications.

Overview

The thesis covers fundamental and advanced topics in reinforcement learning, including policy gradient methods, actor-critic algorithms, and their applications to complex control problems. Through theoretical analysis and empirical experiments, this work contributes to the understanding of how RL agents learn optimal behaviors in challenging environments.

Key Contributions

• Implementation of Soft Actor-Critic (SAC) algorithm for continuous control tasks
• Analysis of exploration-exploitation trade-offs in different scenarios
• Practical applications demonstrating the effectiveness of modern RL techniques
• Comprehensive evaluation metrics and performance benchmarks

Methodology

The research employs a rigorous experimental methodology, combining theoretical foundations with practical implementations. The Soft Actor-Critic algorithm was chosen for its ability to handle continuous action spaces and its sample efficiency compared to traditional policy gradient methods.

Results and Conclusions

This work opens up several avenues for future research, including the application of these techniques to more complex real-world problems and the exploration of hybrid approaches combining RL with other machine learning paradigms.