Reinforcement learning is getting to a state where it is being considered for use within public policy and government services. In this talk, we examine the public sector use-cases where reinforcement learning is being used (or could be used in the …
Talk based on the paper, 'Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning' which was written with co-authors: Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau. I reflect on the journey …
Talk based on paper with co-authors: Joshua Romoff, Ahmed Touati, Emma Brunskill, Joelle Pineau, and Yann Ollivier. In many finite horizon episodic reinforcement learning (RL) settings, it is desirable to optimize for the undiscounted return - in …
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 …