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» Reinforcement Learning: An Introduction
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NIPS
2008
13 years 9 months ago
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation
Actor-critic algorithms for reinforcement learning are achieving renewed popularity due to their good convergence properties in situations where other approaches often fail (e.g.,...
Dotan Di Castro, Dmitry Volkinshtein, Ron Meir
ICGA
2008
100views Optimization» more  ICGA 2008»
13 years 8 months ago
Learning the Piece Values for Three Chess Variants
A set of experiments for learning the values of chess pieces is described for the popular chess variants Crazyhouse Chess, Suicide Chess, and Atomic Chess. We follow an establishe...
Sacha Droste, Johannes Fürnkranz
ACL
2010
13 years 6 months ago
Reading between the Lines: Learning to Map High-Level Instructions to Commands
In this paper, we address the task of mapping high-level instructions to sequences of commands in an external environment. Processing these instructions is challenging--they posit...
S. R. K. Branavan, Luke S. Zettlemoyer, Regina Bar...
ECAI
2006
Springer
14 years 7 hour ago
Using Emotions for Behaviour-Selection Learning
Emotions play a very important role in human behaviour and social interaction. In this paper we present a control architecture which uses emotions in the behaviour selection proces...
Maria Malfaz, Miguel Angel Salichs
SMC
2007
IEEE
118views Control Systems» more  SMC 2007»
14 years 2 months ago
One-class learning with multi-objective genetic programming
One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multiobjective genetic programming (GP) all...
Robert Curry, Malcolm I. Heywood