Applications are invited for a Postdoc at SequeL, Inria Lille. 



Keywords: reinforcement learning, multi-armed bandit, transfer learning, 
exploration-exploitation, non-stationary environments representation learning, 
hierarchical learning. 



Research Topic: Multi-armed bandit and reinforcement learning 

The main objective of this postdoc position is to advance the state-of-the-art 
in the field of multi-armed bandit and reinforcement learning (RL) in general, 
with a particular focus on the development of novel transfer learning 
algorithms. 



Reinforcement learning (RL) formalizes the problem of learning an optimal 
behavior policy from the experience directly collected from an unknown 
environment. Such general model already provides powerful tools that can be 
used to learn from data in a very diverse range of applications (e.g., see 
successful applications of RL to computer games, energy management, logistics, 
and autonomous robotics). Nonetheless, practical limitations of current 
algorithms encouraged research in developing efficient ways to integrate expert 
prior knowledge into the learning process. Although this improves the 
performance of RL algorithms, it dramatically reduces their autonomy, since it 
requires a constant supervision by a domain expert. A solution to this problem 
is provided by transfer learning , i.e., extract knowledge from direct 
experience and transfer it through different tasks to improve the learning 
process . 



The research is postdoc’s research activity will focus on (but not limited to) 
three main elements of transfer in RL: exploration-exploitation strategies in 
changing environments, transfer of general representation, hierarchical 
learning. The research will be primarily theoretical and algorithmic in the 
attempt of defining new rigorous and principled solutions to transfer in RL 
and/or improve existing solutions in multi-armed bandit and RL. 



Profile 

By the time of the beginning of the postdoc, the applicant should have a Ph.D. 
in Computer Science, Statistics, or related fields with background in 
reinforcement learning, bandits, or optimization. Preference will go to 
candidates with strong mathematical background and good publication record. The 
working language in the lab is English. 



How to apply 

The application should include a brief description of research interests and 
past experience, a CV, degrees and grades, a copy of the PhD thesis (or a draft 
thereof), motivation letter (short but pertinent to this call), relevant 
publications, and other relevant documents. Candidates should provide letter(s) 
of recommendation and contact information to reference persons. Please send 
your application in one single pdf to alessandro.lazaric-at-inria.fr . The 
deadline for the application is March 15 , 2016. The final decision will be 
communicated at the beginning of April. 

    * Application closing date: March 15 , 2016 
    * Interviews: shortly after the deadline 
    * Duration: 1 year renewable for 1 year more 
    * Starting date: May 1st, 2016 (flexible) 
    * Contact: Alessandro Lazaric 
    * Place: SequeL, Inria Lille - Nord Europe 



Working environment 

The postdoc will work at SequeL ( https://sequel.lille.inria.fr/ ) lab at Inria 
Lille - Nord Europe located in Lille. Inria ( http://www.inria.fr/ ) is 
France's leading institution in Computer Science, with over 2800 scientists 
employed, of which around 250 in Lille. Lille is the capital of the north of 
France, a metropolis with 1 million inhabitants, with excellent train 
connection to Brussels (30 min), Paris (1h) and London (1h30). The research 
team SequeL (Sequential Learning) is composed of about 20 members working in 
machine learning, notably in reinforcement learning, multi-armed bandit, 
statistical learning, and sequence prediction. The postdoc grant is co-funded 
by the ANR ExTra-Learn project, which is entirely focused on the problem of 
transfer in RL. 

Benefits 

    * Salary: 2621 € 
    * Salary after taxes : around 2115.29€ 
    * Possibility of French courses 
    * Help for housing 
    * Participation for public transport 
    * Scientific Resident card and help for husband/wife visa 
_______________________________________________
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai

Reply via email to