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Artificial Intelligence
 
Data Science
 
Algorithms

[Archimedes Talks Series] Can Q-learning be improved with Advice?

Dates
2024-06-12 11:00 - 13:00
Venue
Artemidos 1 - Amphitheater

Archimedes is proud to host a Talk on "Can Q-learning be improved with Advice?" by Noah Golowich(MIT) as part of our Prediction Study Group this following Wednesday at 11am.

Title: Can Q-learning be improved with Advice?

Presenter: Noah Golowich, Massachusetts Institute of Technology

Abstract: Despite rapid progress in theoretical reinforcement learning (RL) over the last few years, most of the known guarantees are worst-case in nature, failing to take advantage of structure that may be known a priori about a given RL problem at hand. In this paper we address the question of whether worst-case lower bounds for regret in online learning of Markov decision processes (MDPs) can be circumvented when information about the MDP, in the form of predictions about its optimal Q-value function, is given to the algorithm. We show that when the predictions about the optimal Q-value function satisfy a reasonably weak condition we call distillation, then we can improve regret bounds by replacing the set of state-action pairs with the set of state-action pairs on which the predictions are grossly inaccurate. This improvement holds for both uniform regret bounds and gap-based ones. Further, we are able to achieve this property with an algorithm that achieves sublinear regret when given arbitrary predictions (i.e., even those which are not a distillation). Our work extends a recent line of work on algorithms with predictions, which has typically focused on simple online problems such as caching and scheduling, to the more complex and general problem of reinforcement learning. 

Bio: Noah Golowich (Massachusetts Institute of Technology) was advised by Constantinos Daskalakis and Ankur Moitra. He completed his A.B. and S.M. at Harvard University. His research interests lie in theoretical machine learning, with a particular focus on the connections between multi-agent learning, game theory, and online learning, and in theoretical reinforcement learning. He is supported by a Fannie & John Hertz Foundation Fellowship and an NSF Graduate Fellowship.

 

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Vision

To position Greece as a leading player in AI and Data Science

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Mission

To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world

 

Welcome to ARCHIMEDES, a vibrant research hub connecting the global AI and Data Science research community fostering groundbreaking research in Greece and beyond. Its dedicated core team, comprising lead researchers, affiliated researchers, Post-Docs, PhDs and interns, is committed to advancing basic and applied research in Artificial Intelligence and its supporting disciplines, including Algorithms, Statistics, Learning Theory, and Game Theory organized around 8 core research areas. By collaborating with Greek and Foreign Universities and Research Institutes, ARCHIMEDES disseminates its research findings fostering knowledge exchange and providing enriching opportunities for students. Leveraging AI to address real-world challenges, ARCHIMEDES promotes innovation within the Greek ecosystem and extends its societal impact. Established in January 2022, as a research unit of the Athena Research Center with support from the Committee Greece 2021, ARCHIMEDES is funded for its first four years by the EU Recovery and Resilience Facility (RRF).

 
 

NEWS

 
Archimedes Flagship Project in Cardiology and AI is Featured in the News!

Archimedes Flagship Project in Cardiology and AI is Featured in the News!

Archimedes Research Unit of the Athena Research Center, Greece, is featured in a recent article in Dnews. This article is about an Archimedes flagship project in cardiology and AI that aims to use "two-dimensional echocardiographic data to develop deep learning tools and improve the treatment of heart problems."

Best Paper Award at FAIEMA 202

Best Paper Award at FAIEMA 202

Vasileios Moustakas, PhD student at the School of Electrical and Computer Engineering - NTUA and Academic Fellow at Archimedes, Athena Research Center, Greece, Konstantinos Cheliotis and Anna Mylona, both MEng students at the School of Electrical and Computer Engineering - NTUA and interns at Archimedes, Athena Research Center, Vassilis Alimisis, Postdoctoral Researcher at Archimedes, Athena Research Center, and Paul Sotiriadis, Lead Researcher at Archimedes, Athena Research Center, and a Professor at the School of Electrical and Computer Engineering - NTUA, received the Best Paper Award (PhD Symposium) at the

Nature Communications Publication on Advanced AI in Biological Research by Giorgos Papanastasiou

Nature Communications Publication on Advanced AI in Biological Research by Giorgos Papanastasiou

Giorgos Papanastasiou, Lead Researcher at the Archimedes Research Unit of the Athena Research Center, Greece,and Faculty Research Fellow at Edinburgh Imaging, at the University of Edinburgh, the Queen’s Medical Research Institute, Edinburgh, UK, has co-published a Nature Communications paper on "Clinical implications of bone marrow adiposity identified by phenome-wide association and Mendelian randomization in the UK Biobank."Prof. Papanastasiou mentions that "this project is a strong testament to the power of augmenting biological research with advanced AI and data science methods."

 
 

The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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