mugga – Page 2 – Machine Learning and Positioning Systems

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

By mugga May 2, 20222022, 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…

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

By mugga May 2, 20222022, Finished, Master Thesis, Reinforcement Learning

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

By mugga May 2, 20222022, Finished, Master Thesis, Reinforcement Learning

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

By mugga May 2, 20222021, Finished, Reinforcement Learning, Student Project

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, 20222021, Finished, Master Thesis, Reinforcement Learning

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…

Posts by mugga

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

Multi-Agent Reinforcement Learning for the Coordination of Base Stations in 6G Networks