(Apologies for multiple postings)
Call for Papers: Workshop on Rich Representations for Reinforcement Learning
Date: August 7th, 2005
in conjunction with ICML'05, Bonn, Germany
Web Site: http://www.cs.waikato.ac.nz/~kurtd/rrfrl/
Overview:
Reinforcement learning (RL) has developed into a primary approach to
learning control strategies for autonomous agents. The majority of RL
work has focused on propositional or attribute-value representations
of states and actions, simple temporal models of action, and
memoryless policy representations. Many problem domains, however, are
not easily represented under these assumptions.
This has led to recent work that studies the use of richer
representations in RL to overcome some of these traditional
limitations. This includes for example: relational reinforcement
learning, where states, actions and learned policies have relational
representations; richer temporal representations of action, such as
options; richer policy representations that incorporate internal
state, such as MAXQ hierarchies; and the recently introduced
predictive state representations where the state of a system is
represented in terms of the predictions of future observations.
The main topic of the workshop will be the application of these (and
possibly other) rich representational formats, the relationships among
them, and their benefits (or drawbacks) for reinforcement learning.
There have been a number of previous workshops that focus on
individual representational items noted above. The goal of this
workshop is mainly to promote interaction between researchers in the
various representational aspects of RL. There is a high diversity of
rich representations and possible approaches, many of which may
mutually benefit one another. This workshop will give researchers the
chance to consider such benefits and highlight some of the key
challenges that remain.
Given the co-location of ICML with ILP this year, we expect attendees
from both conferences to participate in the workshop as the topic
intersects with interests of both, in particular the incorporation of
relational and logical representations into RL.
Some example topics/issues that could be addressed include:
* New algorithms for exploiting rich representations to the
fullest. When is it possible to design algorithms for rich
representations by reduction to traditional techniques?
* When and how does reinforcement learning benefit from rich
representations? Specific real-world successes and failures are
of particular interest.
* What is the influence of rich representations on the
(re-)usability of reinforcement learning results, or transfer
learning (for example through goal parameterization)?
* Should the introduction of rich representations in reinforcement
learning be accompanied by different learning goals (such as
policy-optimality) to keep the learning problems feasible?
* How should we evaluate new algorithms for rich representations?
Specific benchmarks that exhibit the weaknesses and benefits of
various representational features are of particular interest.
* How can RL benefit from/contribute to existing models and
techniques used for (decision-theoretic) planning and agents
that already use richer representations, but lack learning?
Submissions Format:
Potential speakers should submit a paper of a maximum of 6 pages in
the ICML paper format. We encourage smaller contributions or summaries
of on-going work, one page abstracts, and position papers on the
topics relevant to the workshop.
To supply the panel planned at the end of the workshop with discussion
topics, we ask each potential presenter and participant to propose, in
advance, a provocative question or claim, with the emphasis on
provocative. We will use the resulting pool of questions, possibly
anonymously, to stimulate discussion as needed. The papers and
provocative questions or claims should be sent by email to
[EMAIL PROTECTED] We will assume that your questions can be
attributed to you unless you request anonymity.
Important Dates:
April 1 Paper submission deadline
April 22 Notification of acceptance
May 13 Final paper deadline
August 7 Workshop date
Organizing Committee:
Kurt Driessens: University of Waikato, Hamilton, New Zealand
Alan Fern: Oregon State University, Corvallis, U.S.A.
Martijn van Otterlo: University of Twente, The Netherlands
Program Committee:
Robert Givan: Purdue University, U.S.A.
Roni Khardon: Tufts University, U.S.A.
Ron Parr: Duke University, U.S.A.
Sridhar Mahadevan:University of Massachusetts, U.S.A.
Satinder Singh: University of Michigan, U.S.A.
Prasad Tadepalli: Oregon State University, U.S.A.
--
Kurt Driessens
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Department of Computer Science University of Waikato
Private Bag 3105 phone: +64 7 838 4791
Hamilton, New Zealand
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"The probability that what I was proposing and what you
say I am proposing is the same thing is rather high."
-- Jan Ramon in a recent email-discussion
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