03
Jun
Colloquium
Explainable Methods for Reinforcement Learning
Jasmina Gajcin, Trinity College Dublin
03.06.2024
4:15 pm - 5:45 pm
LMU Department of Statistics and via zoom
Deep reinforcement learning (DRL) algorithms have been successfully devel- oped for many high-risk real-life tasks in many fields such as autonomous driving, healthcare and finance. However, these algorithms rely on neural networks, making their decisions difficult to understand and interpret.
In this talk, I will cover some of the main challenges for developing explainable DRL methods, especially focusing on the difference between supervised and reinforcement learning from the perspective of explainability. Additionally, a part of this talk will be focused on counterfactual explanations in RL. Counterfactual explanations are a powerful explanation method and can explain outcomes by contrasting them with similar events which led to a different outcome. The talk will delve into how counterfactual explanations can be utilized in an RL setting.
Related
Colloquium • 05.02.2025 • LMU Department of Statistics and via zoom
TBA
Colloquium at the LMU Department of Statistics with Isabel Valera (Saarland University in Saarbrücken).
Colloquium • 29.01.2025 • LMU Department of Statistics and via zoom
TBA
Colloquium at the LMU Department of Statistics with Sophie Langer (University of Twente).
Colloquium • 15.01.2025 • LMU Department of Statistics and via zoom
TBA
Colloquium at the LMU Department of Statistics with Sonja Greven (HU Berlin).
Colloquium • 11.12.2024 • LMU Department of Statistics and via zoom
TBA
Colloquium at the LMU Department of Statistics with Stijn Vansteelandt (Ghent University).
Munich AI Lectures • 25.11.2024 • Große Aula der LMU Geschwister-Scholl-Platz 1, Room 120 80539 München
The Mathematical Universe behind Deep Neural Networks
Join us on Nov 25 for Prof. Helmut Bölcskei’s lecture on the mathematical foundations driving deep neural networks, hosted by Bavarian AI at LMU.