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Reinforcement Learning for Climate Change
In this project, we aim to get an overview of how reinforcement learning can be applied to tackle climate change problems. The goal is to then explore an idea that addresses a selected problem.
The changing climate is the most urgent crisis our society is facing. The recently released IPCC’s Sixth Assessment Report (AR6 [1]) once again highlights the pressing need for actions on political, industrial, and societal levels. The promiscuous nature of the problem calls for innovative solutions that will help counteract the destruction that has already been caused to our climate.
The goal of this project is to categorize different types of problems where Reinforcement Learning (RL) could be beneficial. We will limit ourselves to modelling problems. In other words: How can RL be used to model environmental, political, industrial or societal processes to increase our understanding of (combating) climate change. This will be particularly done via a literature review.
Depending on the laid-out scope of the thesis, a prototype of a selected idea will be implemented. The project is purposely posed in quite an open fashion and challenges the creativity of the student. It is as much about problem formulation as answering the problem.
**Tasks**
In particular, the student will
- Carry out a literature study on the current state-of-the-art of using reinforcement learning to model climate change.
- Create a categorization of different problems to which reinforcement learning could be applied to.
- Do a virtual case-study of a potential solution to a selected problem.
- Depending on the scope of the thesis implement a model of the proposed solution to the chosen problem.
**Requirements**
We are looking for independent students who
- are motivated to learn more about the climate crisis.
- are willing to investigate how reinforcement learning techniques can be used to tackle challenges regarding climate change.
- have a creative mindset.
- have background knowledge about machine learning and reinforcement learning.
We do not impose any requirements on the field of study of the students. Hence, we are also accepting applications from other departments.
The projects are research-oriented, and we encourage students to submit to top-tier conferences. We work closely with students during their projects and the thesis work is a great introduction to PhD positions in our lab.
**Reference**
[1] AR6 Climate Change 2021: The Physical Science Basis, https://www.ipcc.ch/report/ar6/wg1/
Image: "Moscow winter is coming" by Cаvin
The changing climate is the most urgent crisis our society is facing. The recently released IPCC’s Sixth Assessment Report (AR6 [1]) once again highlights the pressing need for actions on political, industrial, and societal levels. The promiscuous nature of the problem calls for innovative solutions that will help counteract the destruction that has already been caused to our climate.
The goal of this project is to categorize different types of problems where Reinforcement Learning (RL) could be beneficial. We will limit ourselves to modelling problems. In other words: How can RL be used to model environmental, political, industrial or societal processes to increase our understanding of (combating) climate change. This will be particularly done via a literature review.
Depending on the laid-out scope of the thesis, a prototype of a selected idea will be implemented. The project is purposely posed in quite an open fashion and challenges the creativity of the student. It is as much about problem formulation as answering the problem.
**Tasks**
In particular, the student will
- Carry out a literature study on the current state-of-the-art of using reinforcement learning to model climate change.
- Create a categorization of different problems to which reinforcement learning could be applied to.
- Do a virtual case-study of a potential solution to a selected problem.
- Depending on the scope of the thesis implement a model of the proposed solution to the chosen problem.
**Requirements**
We are looking for independent students who
- are motivated to learn more about the climate crisis. - are willing to investigate how reinforcement learning techniques can be used to tackle challenges regarding climate change. - have a creative mindset. - have background knowledge about machine learning and reinforcement learning.
We do not impose any requirements on the field of study of the students. Hence, we are also accepting applications from other departments.
The projects are research-oriented, and we encourage students to submit to top-tier conferences. We work closely with students during their projects and the thesis work is a great introduction to PhD positions in our lab.
**Reference**
[1] AR6 Climate Change 2021: The Physical Science Basis, https://www.ipcc.ch/report/ar6/wg1/