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SPiRL 2019 : Workshop on Structure and Priors in Reinforcement Learning (SPiRL) at ICLR 2019

New Orleans
Event Date: May 6, 2019 - May 6, 2019
Submission Deadline: Mar 7, 2019
Notification of Acceptance: Mar 28, 2019
Camera Ready Version Due : May 2, 2019

Call For Paper

Dear all,

We're excited to share the call for papers for our upcoming
workshop on structure and priors in reinforcement learning at ICLR
2019. The submission deadline for a 5-page extended abstract is 3/7.

SPiRL Co-organizers


Workshop on “Structure & Priors in Reinforcement Learning” (SPiRL) at ICLR 2019
Monday, May 6th, 09:00 AM – 06:00 PM CST
Room R4, Ernest N. Morial Convention Center, New Orleans

Call for Extended Abstracts

A powerful solution to the problem of generalization and sample
complexity in reinforcement learning (RL) is the deliberate use
of inductive bias. There has been a recent resurgence of interest in
methods of imposing or learning inductive bias in RL in the form of
structure and priors, including, for example, prior distributions for
Bayesian inference, learned hyperparameters in a multi-task or
meta-learning setup, or structural constraints such as temporal
abstraction or hierarchy.

The goal of this workshop is to bring together researchers across a
variety of domains, including RL and machine learning practitioners,
neuroscientists, and cognitive scientists, to discuss the role that
structure and priors play in RL. We invite the submission of abstracts
on topics including, but not limited to:

- Bayesian inference as used in RL
- meta-RL
- transfer learning in RL
- modularity and compositionality
- hierarchical RL
- temporal abstraction
- structured state and action abstractions
- sequential decision-making in humans
- reinforcement processes in the brain

We also invite abstracts that address the following questions directly:

- What is the trade-off between generality and the use of structure
and priors in RL, in the context of specific tasks or in general, and
how can we evaluate this in practice?

- What are the practical or theoretical implications of specific ways
of imposing or learning structure or priors in RL?

- How can we learn data-driven structure and priors for RL (via
transfer in RL, meta-RL, or multi-task RL)?

- How can the different communities (including cognitive science, neuroscience, and machine learning) benefit from collaborative research on these topics?

Important Dates

Extended abstract deadline: Thursday, March 7th, 2019, 11:59 PM anywhere on Earth
Decision notification: Thursday, March 28th, 2019
Camera-ready deadline: Thursday, May 2nd, 2019, 11:59 PM anywhere on Earth

Abstract Format

Extended abstracts should be a short research paper of at most 5 pages
long (excluding references or appendix) in PDF format. Abstracts must
be anonymized; the review process will be double-blind.

Please see the CfP on the workshop website ( for more details.

Please submit your extended abstracts via CMT( by the deadline given above.

Presentation Details

All accepted abstracts will be presented in the form of a poster. A
few select contributions will additionally be given as contributed
talks. Accepted papers will be posted in a non-archival format on the
workshop website.

Workshop Committee

Pierre-Luc Bacon (Stanford)
Marc Deisenroth (Imperial College London)
Chelsea Finn (UC Berkeley/Google Brain/Stanford)
Erin Grant (UC Berkeley)
Tom Griffiths (Princeton)
Abhishek Gupta (UC Berkeley)
Nicolas Heess (DeepMind)
Michael Littman (Brown)
Junhyuk Oh (DeepMind)

If you have any further questions, please contact the SPiRL 2019
committee at

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