The last decade has witnessed a series of technological advances such as social networks, cloud servers, personalized advertising, autonomous cars, personalized healthcare, robotics and security systems, just to name a few. These new technologies have in turn substantially reshaped our demands from reinforcement learning systems, defining novel yet urgent challenges. In response, a wealth of novel ideas and trends have emerged, tackling problems that include modelling rich and high-dimensional dynamics, life-long learning, resource-bounded planning, and multi-agent cooperation.
Goals
The objective of the workshop is to provide a platform for researchers from various areas (e.g., deep learning, game theory, robotics, computational neuroscience, information theory, Bayesian modelling) to disseminate and exchange ideas, evaluating their advantages and caveats. In particular, we will ask participants to address the following questions:
1) What is the future of reinforcement learning?
2) What are the most important challenges?
3) What tools do we need the most?
A final panel discussion will then review the provided answers and focus on elaborating a list of trends and future challenges. Recent advances will be presented in short talks and a poster session based on contributed material.
Call for Contributions
We are seeking two types of contributions:
a) Abstracts on a broad range of topics in reinforcement learning and relevant applications. Abstracts should be (preferrably) two pages in NIPS format. All accepted abstracts will be presented as posters and considered for oral presentation. Send your abstract to ope@seas.upenn.edu with the subject line “NIPS Trends in RL”.
b) Expert opinions (< 500 chars.) about the future of RL and its most important challenges. We seek to get a general picture of the state of RL. We will publish all selected opinions on our website, and they will provide the background material for our workshop. Submit your opinion here.
Important Dates
Registration: See NIPS website
Workshop: Saturday, December 13, 2014, 8:30am – 6:30pm.
Submission Deadline for Abstracts: October 17, 2014. (Passed)
Submission Deadline for Expert Opinions: Open.
Notification of acceptance: October 31, 2014.
Novel Trends and Applications in Reinforcement Learning 