Skip to content
Machine Learning and Positioning Systems
  • Home
  • Team
    • Application Guidelines
  • Publications
  • Research
    • Research Projects
    • Datasets
  • Teaching
    • Thesis
    • Student Projects
    • Courses
      • RL@FAU2025
      • RL@FAU2024
      • RL@FAU2023
      • RL@FAU2022
      • RL@FAU2021
      • RL@LMU2020
      • DataFest: Tool-Tracking with Machine Learning

Reinforcement Learning

Michael Girstl: Safe Reinforcement learning using Successor representations

Posted by By mugga May 4, 2025Posted in2025, Master Thesis, Reinforcement Learning, Running
Reinforcement Learning (RL) has achieved remarkable success in various domains, from game playing to robotics. However, in safety-critical applications such as autonomous driving or medical treatment, traditional RL algorithms may…
Read More

Haris Asif: Quantum Circuit Optimization via Hierarchical Reinforcement Learning (FAU Erlangen-Nürnberg, 2024)

Posted by By mugga May 4, 2025Posted in2024, Finished, Master Thesis, Reinforcement Learning
Motivation / Related Work Optimizing quantum circuits is essential for various applications, such as chemical simulations, due to the inherent complexity of quantum systems and the current limitations of quantum…
Read More

Thorsten Bescher: Schedule-Net and Gumbel-Alpha-Zero Play-to-Plan: a Hybrid Approach for the Job-Shop Scheduling Problem (FAU Erlangen-Nürnberg, 2024)

Posted by By mugga May 4, 2025Posted in2024, Finished, Master Thesis, Reinforcement Learning
Motivation / Related Work Recently, reinforcement learning (RL) is applied frequently for combinatorial optimization problems [1] with real world applications, e.g., the job-shop scheduling problem (JSSP) or the travelling salesman…
Read More

Nicolas Kolbenschlag: Automated Generation of Block-Encodings for Quantum Policy Iteration (FAU Erlangen-Nürnberg, 2024)

Posted by By mugga May 4, 2025Posted in2024, Finished, Master Thesis, Reinforcement Learning
Motivation The rise of quantum computing has offered new opportunities to achieve computational tasks that classical computers cannot. However, to achieve quantum advantage over classical algorithms, it is necessary to…
Read More

Alexander Mattick: Reinforcement Learning for Node Selection in Branch-and-Bound (FAU Erlangen-Nürnberg, 2023)

Posted by By mugga May 4, 2025Posted in2023, Finished, Master Thesis, Reinforcement Learning
1 Introduction The branch-and-bound algorithm is a fundamental solver for linear mixed integer programming problems. It is used to solve optimization problems with a combination of continuous and integer variables.…
Read More

Yongxu Ren: Variational Quantum Compiling with (Deep) Reinforcement Learning (FAU Erlangen-Nürnberg, 2023)

Posted by By mugga November 1, 2022Posted in2023, Finished, Master Thesis, Reinforcement Learning
MotivationQuantum computing promises to revolutionize many areas that are hard or impossible to approach with tra- ditional computers. However, due to rigid hardware restrictions and noise sensitivity of currently available…
Read More

Alexander Mattick: Beam Tracking as a Time-Varying Reinforcement Learning Problem

Posted by By mugga November 1, 2022Posted in2023, 5G & 6G, Finished, Reinforcement Learning, Student Project
The envisioned transition to 5G and 6G technologies have started to transform the properties of established communication networks. In the core of this transformation lies the capability of Base Stations…
Read More

Abhinav Singh: Safe Imitation Learning for Beam Tracking

Posted by By mugga November 1, 2022Posted in2022, 5G & 6G, Finished, Reinforcement Learning, Student Project
The envisioned transition to 5G and 6G technologies have started to transform the properties of established communication networks. In the core of this transformation lies the capability of Base Stations…
Read More

Lukas Frieß: Model-based Reinforcement Learning with First-Principle Models (FAU Erlangen-Nürnberg, 2021)

Posted by By mugga May 2, 2022Posted in2021, Finished, Master Thesis, Reinforcement Learning
Motivation Reinforcement learning (RL) is increasingly used in robotics to learn complex tasks from repeated interactions with the environment. For example, a mobile robot can learn to avoid an obstacle…
Read More

Dinesh Parthasarathy: Safe Monte Carlo Tree Search using Learned Safety Critics (FAU Erlangen-Nürnberg, 2022)

Posted by By mugga May 2, 2022Posted in2022, Finished, Master Thesis, Reinforcement Learning
Thesis available for download here: Dinesh Parthsarathy: Safe Monte Carlo Tree Search using Learned Safety Critics Background Reinforcement Learning and planning are two research fields that have shown great success…
Read More

Posts pagination

1 2 Next page
Scroll to Top