Project-Type

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

By mugga May 2, 2022Finished, Master Thesis, Student-Projects, Uncategorized

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…

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

By mugga May 2, 2022Finished, Master Thesis, Student-Projects

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…

Lars Ulrich: Analyzing the Effect of Design Choices in Model-based Reinforcement Learning (FAU Erlangen-Nürnberg, 2022)

By mugga May 2, 2022Finished, Master Thesis, Student-Projects, Uncategorized

Hyeyoung Park: Interpretable Decision Tree Extraction using Imitation Learning and Locally Adaptive Optimization (LMU München, 2022)

By mugga May 2, 2022Finished, Project, Student-Projects, Uncategorized

Hyeyoung Park: Towards Interpretable (and Robust) Reinforcement Learning Policies through Local Lipschitzness and Randomization

By mugga May 2, 2022Finished, Project, Student-Projects

Reinforcement Learning is broadly applicable for diverse tasks across many domains. On many problems, it has achieved superhuman performance [5]. However, the black-box neural networks used by modern RL algorithms…

Sebastian Fischer: Back to the Basics: Offline Reinforcement Learning with Least-Squares Methods for Policy Iteration (LMU München, 2021)

By mugga May 2, 2022Finished, Master Thesis, Student-Projects

Recently, offline (sometimes also called ‘batch’) Reinforcement Learning (RL) algorithms have gained significant research traction [1]. The reason behind this is that – unlike in the classical Reinforcement Learning formulation…

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

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

Lars Ulrich: Analyzing the Effect of Design Choices in Model-based Reinforcement Learning (FAU Erlangen-Nürnberg, 2022)

Hyeyoung Park: Interpretable Decision Tree Extraction using Imitation Learning and Locally Adaptive Optimization (LMU München, 2022)

Hyeyoung Park: Towards Interpretable (and Robust) Reinforcement Learning Policies through Local Lipschitzness and Randomization

Sebastian Fischer: Back to the Basics: Offline Reinforcement Learning with Least-Squares Methods for Policy Iteration (LMU München, 2021)

Matthias Gruber: Learning to Avoid your Supervisor (LMU München, 2021)

Daniel Landgraf: Hierarchical Learning and Model Predictive Control (FAU Erlangen-Nürnberg, 2020)

Ilona Bamiller: Benchmarking Offline Reinforcement Learning on an Autonomous Driving Application (LMU München, 2021)

Meta Reinforcement Learning for Optimization of Electric Circuit Parameters