Reinforcement Learning Day

Reinforcement Learning Day


Reinforcement Learning Day 2018 will share the latest research on learning to make decisions based on feedback.

Reinforcement learning is the study of decision making with consequences over time. The topic draws together multi-disciplinary efforts from computer science, cognitive science, mathematics, psychology, economics, control theory, and neuroscience. The common thread through all of these studies is: how do natural and artificial systems learn to make decisions in complex environments based on external, and possibly delayed, feedback.

This workshop features talks by nine outstanding speakers whose research covers a broad swath of the topic, from statistics to psychology, from computer science to control. A key objective is to bring together the research communities of all these areas to learn from each other and build on the latest knowledge.

Committee Chairs

Hal Daumé III, Microsoft Research
Akshay Krishnamurthy, Microsoft Research


Shipra Agrawal, Columbia University
Byron Boots, Georgia Institute of Technology
Marc-Alexandre Côté, Microsoft Research
Debadeepta Dey, Microsoft Research
Miro Dudík, Microsoft Research
Catherine Hartley, New York University
Raia Hadsell, Google DeepMind
Katja Hofmann, Microsoft Research
Michael Littman, Brown University

Local Organizer

KyungHyun Cho, New York University


Monday, September 24, 2018

Time (PDT) Session Speaker
9:00 AM–9:10 AM Welcome Portrait of John Langford John Langford
Microsoft Research NYC
9:10 AM–9:55 AM Programming Agents via Evaluative Feedback Portrait of Michael Littman Michael Littman
Brown University
9:55 AM–10:25 AM Directions and Challenges in Multi-Task Reinforcement Learning Portrait of Katja Hofmann Katja Hofmann
Microsoft Research Cambridge
10:25 AM–10:45 AM Break
10:45 AM–11:30 AM Portrait of Catherine Hartley Catherine Hartley
New York University
11:30 AM–12:15 PM Machine Learning for Robot Perception, Planning, and Control Portrait of Byron Boots Byron Boots
Georgia Institute of Technology
12:15 PM–1:45 PM Lunch
1:45 PM–2:15 PM Imitating the Clairvoyant Oracle: From Information Gathering to Grounded Visual Navigation via Natural Language Portrait of Debadeepta Dey Debadeepta Dey
Microsoft Research Redmond
2:15 PM–3:00 PM Representation, Memory, and Control: The Challenges of Deep RL in Complex Environments Portrait of Raia Hadsell Raia Hadsell
Google DeepMind
3:00 PM–3:30 PM Trying to Solve Text-based Games Using Reinforcement Learning Portrait of Marc-Alexandre Côté Marc-Alexandre Côté
Microsoft Research Montréal
3:30 PM–3:50 PM Break
3:50 PM–4:20 PM Hierarchical Imitation and Reinforcement Learning Portrait of Miro Dudík Miro Dudík
Microsoft Research NYC
4:20 PM–5:05 PM Portrait of Shipra Agrawal Shipra Agrawal
Columbia University
5:05 PM–5:15 PM Concluding Remarks Portrait of Hal Daumé III Hal Daumé III
Microsoft Research NYC