Summary

Many of the successes in deep learning build upon rich supervision. Reinforcement learning (RL) is no exception to this: algorithms for locomotion, manipulation, and game playing often rely on carefully crafted reward functions that guide the agent. But defining dense rewards becomes impractical for complex tasks. Moreover, attempts to do so frequently result in agents exploiting human error in the specification. To scale RL to the next level of difficulty, agents will have to learn autonomously in the absence of rewards.

We define task-agnostic reinforcement learning (TARL) as learning in an environment without rewards to later quickly solve down-steam tasks. Active research questions in TARL include designing objectives for intrinsic motivation and exploration, learning unsupervised task or goal spaces, global exploration, learning world models, and unsupervised skill discovery. The main goal of this workshop is to bring together researchers in RL and investigate novel directions to learning task-agnostic representations with the objective of advancing the field towards more scalable and effective solutions in RL.

We invite paper submissions in the following categories to present at the workshop:

Speakers

Pierre-Yves Oudeyer
Inria
Research Director
Chelsea Finn
Google, Berkeley, Stanford
Assistant Professor
Neil Bramley
University of Edinburgh
Assistant Professor
Doina Precup
McGill, MILA, DeepMind
Professor
Martin Riedmiller
DeepMind
Research Scientist

Dates

Event Date
Submission deadline 29 March 2019 (11:59 pm AOE)
Notifications 29 April 2019
Camera ready 04 May 2019 (11:59 pm AOE)
Workshop 06 May 2019

Submissions

Papers should be in anonymous ICLR style and up to 5 pages, with an unlimited number of pages for references and appendix. Accepted papers will be made available on the workshop website and selected authors will be offered a 15 min talk at the workshop. This does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work at journals or conferences.

Start a submission: https://cmt3.research.microsoft.com/tarl2019

Sponsors

We thank our sponsors for making this workshop possible:

Schedule

Time Event
08:45 Introduction
09:00 Invited Talk 1 – TBD
09:30 Invited Talk 2 – TBD
10:00 Poster Session 1 + Coffee Break
11:00 Contributed Talk 1 – TBD
11:20 Contributed Talk 2 – TBD
11:40 Contributed Talk 3 – TBD
12:00 Lunch Break
13:30 Invited Talk 3 – TBD
14:00 Contributed Talk 4 – TBD
14:20 Contributed Talk 5 – TBD
14:40 Contributed Talk 6 – TBD
15:00 Poster Session 2 + Coffee Break
16:00 Invited Talk 4 – TBD
16:30 Invited Talk 5 – TBD
17:00 Panel discussion
18:00 End

Organizers

Danijar Hafner
Google Brain
University of Toronto
Amy Zhang
Facebook AI Research
McGill University
Ahmed Touati
University of Montreal
Deepak Pathak
UC Berkeley
Frederik Ebert
UC Berkeley
Rowan McAllister
UC Berkeley
Roberto Calandra
Facebook AI Research
Marc G. Bellemare
Google Brain
McGill University
Raia Hadsell
DeepMind
Alessandro Lazaric
Facebook AI Research
Joelle Pineau
Facebook AI Research
McGill University

For question, please contact us at: taskagnosticrl@gmail.com