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Domain Adaptation of Different Dynamics in Reinforcement Learning
Reinforcement learning (RL) has demonstrated significant advancements in recent years. However, the performance of RL agents can be heavily influenced by domain shift, limiting its real-world applications. To address the challenge of domain shift in different fields, domain adaptation techniques have been proposed. However, while domain adaptation has been extensively studied in computer vision, it remains relatively unexplored in RL. In particular, the unique characteristics of RL environments require a stronger focus on adapting to varying dynamics, rather than simply adjusting to different state spaces. This aspect has received comparatively less attention, and viable solutions remain under investigation. Moreover, the ability to adapt to dynamic environments is especially critical in industrial settings, where systems are diverse and constantly evolving due to degradation. The challenge of domain adaptation in RL, therefore, presents a significant research opportunity with the potential for substantial real-world impact.
Keywords: Reinforcement Learning, Domain Adaptation, Deep Learning
The goal of this project is to investigate domain adaptation techniques for different dynamics in reinforcement learning, with a specific focus on industrial systems. The project will involve the following tasks:
1. Conduct a comprehensive literature review on domain adaptation in reinforcement learning, identifying the key differences and challenges compared to other areas such as computer vision.
2. Develop a theoretical framework for domain adaptation in RL, focusing on the adaptation to different dynamics and compared with other methods.
3. Implement and evaluate the proposed method on general RL benchmark[1,2], and if possible, simulated environment of industrial systems.
4. Document the findings and contribute to research publications on the topic of domain adaptation in reinforcement learning.
**Requirements:**
The candidate for this project should have:
1. A strong motivation to work on reinforcement learning and domain adaptation.
2. Proficiency in Python and familiarity with RL libraries (e.g., TensorFlow, PyTorch, OpenAI Gym).
3. Willingness to work on real-world application case studies.
4. Good communication skills and the ability to collaborate with others.
5. Currently enrolled as a student.
The goal of this project is to investigate domain adaptation techniques for different dynamics in reinforcement learning, with a specific focus on industrial systems. The project will involve the following tasks: 1. Conduct a comprehensive literature review on domain adaptation in reinforcement learning, identifying the key differences and challenges compared to other areas such as computer vision. 2. Develop a theoretical framework for domain adaptation in RL, focusing on the adaptation to different dynamics and compared with other methods. 3. Implement and evaluate the proposed method on general RL benchmark[1,2], and if possible, simulated environment of industrial systems. 4. Document the findings and contribute to research publications on the topic of domain adaptation in reinforcement learning. **Requirements:** The candidate for this project should have: 1. A strong motivation to work on reinforcement learning and domain adaptation. 2. Proficiency in Python and familiarity with RL libraries (e.g., TensorFlow, PyTorch, OpenAI Gym). 3. Willingness to work on real-world application case studies. 4. Good communication skills and the ability to collaborate with others. 5. Currently enrolled as a student.
Not specified
Please send per email tian@ibi.baug.ethz.ch in a single PDF, a motivation letter (how is your profile relevant to the project and how is the project relevant for your career goals) and your transcripts.
Please send per email tian@ibi.baug.ethz.ch in a single PDF, a motivation letter (how is your profile relevant to the project and how is the project relevant for your career goals) and your transcripts.