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» Variational methods for Reinforcement Learning
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ROBOCUP
2000
Springer
130views Robotics» more  ROBOCUP 2000»
13 years 11 months ago
Improvement Continuous Valued Q-learning and Its Application to Vision Guided Behavior Acquisition
Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to converge. This makes it difficult to be applied to re...
Yasutake Takahashi, Masanori Takeda, Minoru Asada
ANOR
2005
80views more  ANOR 2005»
13 years 7 months ago
Entropic Penalties in Finite Games
The main objects here are finite-strategy games in which entropic terms are subtracted from the payoffs. After such subtraction each Nash equilibrium solves an explicit, unconstra...
Sjur Didrik Flåm, E. Cavazzuti
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
13 years 9 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...
ICML
2004
IEEE
14 years 8 months ago
Multi-task feature and kernel selection for SVMs
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is ...
Tony Jebara
ESANN
2007
13 years 9 months ago
The Recurrent Control Neural Network
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
Anton Maximilian Schäfer, Steffen Udluft, Han...