[robotics-worldwide] [meetings] Extended Deadline -- Call for papers: AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL)

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[robotics-worldwide] [meetings] Extended Deadline -- Call for papers: AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL)

Shayegan Omidshafiei
[Deadline extended -- see below for details!]


AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent
Reinforcement Learning (COMARL)

March 23-25 2020, Stanford University in Palo Alto, California, USA.


https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_corp_view_comarl-2Daaai-2D2020_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=mkzRV-vlsOJwcZaFGMHbgKdfc2YLKyLxpdEf8QxcPk4&s=lSXcJPgjp7THJDaF-rgsfeR32n91lOaCrHKcsfo7K80&e= 


Key Dates:


Submission: *Extended to November 11th, 2019, 23:59 GMT*

Notification: December 6th, 2019

Symposium: March 23-25 2020


Call for position papers to define a topic of study at the symposium:


We live in a multi-agent world and to be successful in that world
intelligent agents will need to learn to take into account the agency of
others. They will need to compete in market places, cooperate in teams,
communicate with others, coordinate their plans, and negotiate outcomes.
Examples include self-driving cars interacting in traffic, personal
assistants acting on behalf of humans and negotiating with other agents,
swarms of unmanned aerial vehicles, financial trading systems, robotic
teams, and household robots.


There has been a lot of great work on multi-agent reinforcement learning
(MARL) in the past decade, but significant challenges remain, including:

   -

   the difficulty of learning an optimal model/policy from a partial signal,
   -

   learning to cooperate/compete in non-stationary environments with
   distributed, simultaneously learning agents,
   -

   the interplay between abstraction and influence of other agents,
   -

   the exploration vs. exploitation dilemma,
   -

   the scalability and effectiveness of learning algorithms,
   -

   avoiding social dilemmas, and
   -

   learning emergent communication.



The purpose of this symposium is to bring together researchers in
multiagent reinforcement learning, but also more widely machine learning
and multiagent systems, to explore some of these and other challenges in
more detail. The main goal is to broaden the scope of MARL research and to
address the fundamental issues that hinder the applicability of MARL for
solving complex real world problems.


We aim to organize an active workshop, with many interactive
(brainstorm/breakout) sessions. We are hopeful that this will form the
basis for ongoing collaborations on these challenges between the attendants
and we aim for several position papers as concrete outcomes.


Authors can submit papers of 1-4 pages that will be reviewed by the
organizing committee. We are looking for position papers that present a
challenge or opportunity for MARL research, which should be on a topic the
authors not only wish to interact on but also ‘work’ on with other
participants during the symposium. We also welcome (preliminary) research
papers that describe new perspectives to dealing with MARL challenges, but
we are not looking for summaries of current research---papers should
clearly state some limitation(s) of current methods and potential ways
these could be overcome. Submissions will be handled through easychair.


Organizing Committee:



Christopher Amato, Northeastern University

Frans Oliehoek, Delft University of Technology

Shayegan Omidshafiei, Google DeepMind

Karl Tuyls, Google DeepMind
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