Causal World Models

Course Information

• Organized by:  Prof. Joschka Boedecker, Julien Brosseit
  
• Kickoff meeting: TBA
• Location: Intelligent Machine-Brain Interface Technology (IMBIT), Nexus Lab 
• HISinOne: 11LE13S-7355-MB
• Language: English
• Email:cwm-seminar@informatik.uni-freiburg.de

Overview

World Models (Ha et al., 2018) are learned simulators used by agents to infer knowlegde, plan and make informed decisions in their environment. They are proving to be an effective approach in reinforcement learning (RL), robotics, generative AI, and other areas of machine learning. After all, they share similarities to how we humans think about our environment. 

While standard approaches to learning world models are able to capture correlations in the data, they may fail under interventions and distribution shifts. Causal World Models (CWM) (Li et al., 2020) are able to capture underlying factors to answer counterfactually queries such as "What would have happened if I had acted differently?" leading to improved generalization, explainability and robustness.

In this seminar, we will take a closer look at CWMs, about their inner workings, and why they are so important for building effective World Models.

Format

The course will be given in person, in the form of a block seminar, where papers are read and presented by students in the form of a scientific poster.

Paper Voting Process

TBA

Timeline

TBA

Paper List

TBA

Resources

Poster guideline as pptx or pdf