Hyeyoung Park: Towards Interpretable (and Robust) Reinforcement Learning Policies through Local Lipschitzness and Randomization
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)
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…
Matthias Gruber: Learning to Avoid your Supervisor (LMU München, 2021)
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…
Daniel Landgraf: Hierarchical Learning and Model Predictive Control (FAU Erlangen-Nürnberg, 2020)
Recent progress in (deep) learning algorithms has shown great potential to learn complex tasks purely from large numbers of samples or interactions with the environment. However, performing such interactions can…
Ilona Bamiller: Benchmarking Offline Reinforcement Learning on an Autonomous Driving Application (LMU München, 2021)
Reinforcement Learning (RL) builds on the idea that an agent learns an optimal behavior through iterative interaction with an environment. In model-free reinforcement learning the agent does not have access…