Forum for Artificial Intelligence


Forum for Artificial Intelligence

[ About FAI   |   Upcoming talks   |   Past talks ]



The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

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[ Upcoming talks ]

Fri, October 4
11:00AM
GDC 6.302
Katerina Fragkiadaki
Carnegie Mellon University
TBD
Fri, November 15
11:00AM
GDC 6.302
Yoav Artzi
Cornell
TBD
Fri, November 22
11:00AM
GDC 6.302
Jacob Andreas
MIT
TBD
Fri, December 6
11:00AM
GDC 6.302
Dan Roth
University of Pennsylvania
TBD
Fri, January 24
11:00AM
GDC 6.302
Danqi Chen
Princeton
TBD
Fri, February 7
11:00AM
GDC 6.302
Dieter Fox
University of Washington
TBD

Friday, October 4, 2019, 11:00AM



GDC 6.302

TBD

Katerina Fragkiadaki   [homepage]

Carnegie Mellon University

TBD

About the speaker:

TBD

Friday, November 15, 2019, 11:00AM



GDC 6.302

TBD

Yoav Artzi   [homepage]

Cornell

TBD

About the speaker:

TBD

Friday, November 22, 2019, 11:00AM



GDC 6.302

TBD

Jacob Andreas   [homepage]

MIT

TBD

About the speaker:

TBD

Friday, December 6, 2019, 11:00AM



GDC 6.302

TBD

Dan Roth   [homepage]

University of Pennsylvania

TBD

About the speaker:

TBD

Friday, January 24, 2020, 11:00AM



GDC 6.302

TBD

Danqi Chen   [homepage]

Princeton

TBD

About the speaker:

TBD

Friday, February 7, 2020, 11:00AM



GDC 6.302

TBD

Dieter Fox   [homepage]

University of Washington

TBD

About the speaker:

TBD


[ Past talks]

Fri, August 30
1:00PM
GDC 6.302
Simone Parisi
TU Darmstadt
Scalable and Autonomous Reinforcement Learning

Friday, August 30, 2019, 1:00PM



GDC 6.302

Scalable and Autonomous Reinforcement Learning

Simone Parisi   [homepage]

TU Darmstadt

Over the course of the last decade, reinforcement learning has developed into a promising tool for learning a large variety of task. A lot of effort has been directed towards scaling reinforcement learning to solve high-dimensional problems, such as robotic tasks with many degrees of freedom or videogames. These advances, however, generally depend on hand-crafted state descriptions, pre-structured parameterized policies, or require large amount of data or human interaction. This pre-structuring is arguably in stark contrast to the goal of autonomous learning. In this talk, I discuss the need of systematic methods to increase the autonomy of traditional learning systems, and focus on the problems of stability when little data is available, the presence of multiple conflicting objectives and high-dimensional input, and the need of novel exploration strategies in reinforcement learning.

About the speaker:

Simone Parisi joined the Intelligent Autonomous System lab on October, 1st, 2014 as a PhD student. His research interests include, amongst others, reinforcement learning, robotics, multi-objective optimization, and intrinsic motivation. During his PhD, Simone is working on Scalable Autonomous Reinforcement Learning (ScARL), developing and evaluating new methods in the field of robotics to guarantee both high degree of autonomy and the ability to solve complex task. Before his PhD, Simone completed his MSc in Computer Science Engineering at the Politecnico di Milano, Italy, and at the University of Queensland, Australia. His thesis, entitled “Study and analysis of policy gradient approaches for multi-objective decision problems, was written under the supervision of Marcello Restelli and Matteo Pirotta.

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