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Abstraction in RL 2016 : Abstraction in Reinforcement Learning: ICML Workshop 2016

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Link: http://rlabstraction2016.wix.com/icml
 
When Jun 23, 2016 - Jun 23, 2016
Where New York, USA
Submission Deadline May 1, 2016
Categories    reinforcement learning   deep learning   temporal extension   state space representations
 

Call For Papers

Dear Colleagues,

We would like to invite you to submit extended abstracts of between 4-6 pages to our 'Abstraction in Reinforcement Learning' ICML 2016 Workshop which will be held in New York City on the 23rd June 2016.

IMPORTANT INFORMATION
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Website: http://rlabstraction2016.wix.com/icml
Date: 23rd June 2016
Location: New York City, USA
Submission deadline: 1st May 2016, 11:59 PM (GMT+2)

OVERVIEW
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Many real-world domains can be modelled using some form of abstraction. An abstraction is an important tool that enables an agent to focus less on the lower level details of a task and more on solving the task at hand. Temporal abstraction (i.e., options or skills) as well as spatial abstraction (i.e., state space representation) are two important examples. The goal of this workshop is to provide a forum to discuss the current challenges in designing as well as learning abstractions in real-world Reinforcement Learning (RL).

SUBMISSION
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The submitted work should be an extended abstract of between 4-6 pages (including references). The submission should be in pdf format and should follow the style guidelines for ICML 2016. The review process is double-blind and the work should be submitted by the latest 1st May 2016, 11:59 PM (GMT+2).

AREAS OF INTEREST
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Reinforcement Learning (RL)
Deep RL
RL options, skills, macro-actions
State-space representations
New benchmark domains for learning abstractions in RL

For more info see our website.

WORKSHOP ORGANIZERS
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Daniel J. Mankowitz - Technion Israel Institute of Technology
Timothy A. Mann - Google Deepmind
Shie Mannor - Technion Israel Institute of Technology

We look forward to reviewing your submissions and hope to see you in NYC!

Kind regards,
Daniel, Tim and Shie

Abstraction in Reinforcement Learning Workshop organizers

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