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Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008, Revised and Selected Papers (Paperback, 2008 ed.)
Sertan Girgin, Manuel Loth, Remi Munos, Philippe Preux, Daniil Ryabko
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R1,560
Discovery Miles 15 600
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Ships in 10 - 15 working days
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Inthesummerof2008,
reinforcementlearningresearchersfromaroundtheworld gathered in the
north of France for a week of talks and discussions on reinfor-
ment learning, on how it could be made more e?cient, applied to a
broader range of applications, and utilized at more abstract and
symbolic levels. As a participant in this 8th European Workshop on
Reinforcement Learning, I was struck by both the quality and
quantity of the presentations. There were four full days of short
talks, over 50 in all, far more than there have been at any p-
vious meeting on reinforcement learning in Europe, or indeed,
anywhere else in the world. There was an air of excitement as
substantial progress was reported in many areas including Computer
Go, robotics, and ?tted methods. Overall, the work reported seemed
to me to be an excellent, broad, and representative sample of
cutting-edge reinforcement learning research. Some of the best of
it is collected and published in this volume. The workshopandthe
paperscollectedhere provideevidence thatthe ?eldof reinforcement
learning remains vigorous and varied. It is appropriate to re?ect
on some of the reasons for this. One is that the ?eld remains
focused on a pr- lem - sequential decision making - without
prejudice as to solution methods. Another is the existence of a
common terminology and body of theory
Reinforcement learning is the problem faced by an agent that must
learn behavior through trial-and-error interactions with a dynamic
environment. Usually, the problem to be solved contains subtasks
that repeat at different regions of the state space. Without any
guidance an agent has to learn the solutions of all subtask
instances independently, which in turn degrades the performance of
the learning process. In this work, we propose two novel approaches
for building the connections between different regions of the
search space. The first approach efficiently discovers abstractions
in the form of conditionally terminating sequences and represents
these abstractions compactly as a single tree structure; this
structure is then used to determine the actions to be executed by
the agent. In the second approach, a similarity function between
states is defined based on the number of common action sequences;
by using this similarity function, updates on the action-value
function of a state are re�ected to all similar states that allows
experience acquired during learning be applied to a broader
context. The effectiveness of both approaches is demonstrated
empirically over various domains.
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