Zahra Sheikhbahaee

Max J. van Duijn

Mahapatra Dwarikanath

Qingying Gao

Ashok Goel

Rebecca R. Goldstein

Matthew C. Gombolay

Gabriel Enrique Gonzalez

Amar Halilovic

Tobias Halmdienst

Mahimul Islam

Julian Jara-Ettinger

Natalie Kastel

Renana Keydar

Ashish K. Khanna

Mahdi Khoramshahi

Jihyun Kim

Mihyeon Kim

Youngbin Kim

Senka Krivic

Nikita Krasnytskyi

Arun Kumar

Junehyoung Kwon

EunJu Lee

Shane Lee

Peter R. Lewis 0001

Xue Li

Yijiang Li

Michal Lewandowski

Nathan Lloyd

Matthew B. Luebbers

Dezhi Luo

Haiyun Lyu

Dwarikanath Mahapatra

Kamal Maheshwari

Mallika Mainali

P. Mathur

Patrick Mederitsch

Shuwa Miura

Manuel Preston de Miranda

Reuth Mirsky

Shreya Mishra

Nina M. Moorman

Katelyn Morrison

John Muchovej

Bernhard Nessler

Felix Nessler

Hieu Minh Jord Nguyen

Abby Ortego

F. Papay

Antoine Pasquali

Hamed Rahimi

C. Raghu

Amanda L. Royka

Stefan Sarkadi

Jaelle Scheuerman

Simon Schmid

Paul Schrater

Anik Sen

Ke Shi

Reid G. Simmons

Nishant Singh

Mason O. Smith

Ramira van der Meulen

Anthia Solaki

Haoran Sun

Viktor Szolga

Matthew E. Taylor

Travis Taylor

Sanne van Waveren

Juan David Vargas

R. Verbrugge

Eitan Wagner

Justin D. Weisz

Ximing Wen

William Yeoh

Wenlong Zhang

Michelle Zhao

Shlomo Zilberstein

2025-05-01

arXiv (publié)

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind

Mouad Abrini

Omri Abend

Dina M. Acklin

Henny Admoni

Gregor Aichinger

Nitay Alon

Zahra Ashktorab

Ashish Atreja

Moises Auron

Alexander Aufreiter

Raghav Awasthi

Soumya Banerjee

Joseph Barnby

Rhea Basappa

Severin Bergsmann

Djallel Bouneffouf

Patrick Callaghan

Marc Cavazza

Thierry Chaminade

Sonia Chernova … (voir 88 de plus)

Mohamed Chetouan

Moumita Choudhury

Axel Cleeremans

J. Cywinski

Fabio Cuzzolin

Hokin Deng

N'yoma Diamond

C. D. Pasquasio

Max J. van Duijn

Mahapatra Dwarikanath

Qingying Gao

Ashok Goel

Rebecca R. Goldstein

Matthew C. Gombolay

Gabriel Enrique Gonzalez

Amar Halilovic

Tobias Halmdienst

Mahimul Islam

Julian Jara-Ettinger

Natalie Kastel

Renana Keydar

Ashish K. Khanna

Mahdi Khoramshahi

Jihyun Kim

Mihyeon Kim

Youngbin Kim

Senka Krivic

Nikita Krasnytskyi

Arun Kumar

Junehyoung Kwon

EunJu Lee

Shane Lee

Peter R. Lewis 0001

Xue Li

Yijiang Li

Michal Lewandowski

Nathan Lloyd

Matthew B. Luebbers

Dezhi Luo

Haiyun Lyu

Dwarikanath Mahapatra

Kamal Maheshwari

Mallika Mainali

P. Mathur

Patrick Mederitsch

Shuwa Miura

Manuel Preston de Miranda

Reuth Mirsky

Shreya Mishra

Nina M. Moorman

Katelyn Morrison

John Muchovej

Bernhard Nessler

Felix Nessler

Hieu Minh Jord Nguyen

Abby Ortego

F. Papay

Antoine Pasquali

Hamed Rahimi

C. Raghu

Amanda L. Royka

Stefan Sarkadi

Jaelle Scheuerman

Simon Schmid

Paul Schrater

Anik Sen

Ke Shi

Reid G. Simmons

Nishant Singh

Mason O. Smith

Ramira van der Meulen

Anthia Solaki

Haoran Sun

Viktor Szolga

Matthew E. Taylor

Travis Taylor

Sanne van Waveren

Juan David Vargas

R. Verbrugge

Eitan Wagner

Justin D. Weisz

Ximing Wen

William Yeoh

Wenlong Zhang

Michelle Zhao

Shlomo Zilberstein

2025-04-28

ArXiv (prépublication)

Learning Robust Representations for Transfer in Reinforcement Learning

Faisal Mohamed

Roger Creus Castanyer

Hongyao Tang

Glen Berseth

Learning transferable representations for deep reinforcement learning (RL) is a challenging problem due to the inherent non-stationarity, di… (voir plus)stribution shift, and unstable training dynamics. To be useful, a transferable representation needs to be robust to such factors. In this work, we introduce a new architecture and training strategy for learning robust representations for transfer learning in RL. We propose leveraging multiple CNN encoders and training them not to specialize in areas of the state space but instead to match each other's representation. We find that learned representations transfer well across many Atari tasks, resulting in better transfer learning performance and data efficiency than training from scratch.

2024-10-10

NeurIPS.cc/2024/Workshop/FITML (poster)

openreview.net

From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.

Adam Safron

Casper Hesp

2023-12-01

Physics of Life Reviews (publié)

From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.

Adam Safron

Casper Hesp

2023-11-17

ArXiv (prépublication)

Attention Schema in Neural Agents

Dianbo Liu

Samuele Bolotta

Mike He Zhu

Yoshua Bengio

Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static s… (voir plus)election of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.

2023-10-27

NeurIPS.cc/2023/Workshop/InfoCog (poster)