Talk based on paper with co-authors: Emma Brunskill. The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be …
Talk based on work with co-authors: Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, Joelle Pineau. The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A …
Benchmarks are particularly useful for characterizing algorithms and determining their usefulness in different settings. Here I highlight the need for more standardization of performance evaluations in inverse reinforcement learning.
In recent years, significant progress has been made in solving challenging problems using deep learning. Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, …
Talk based on work with co-authors: Riashat Islam, Maziar Gomrokchi and Doina Precup. A brief discussion on some difficulties that students may encounter in reproducing modern policy gradient methods in continuous control tasks and best practices …
A practical tutorial session on implementing and running policy gradient methods for continuous control tasks.