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» Models of active learning in group-structured state spaces
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ICML
2003
IEEE
16 years 5 months ago
Exploration in Metric State Spaces
We present metric?? , a provably near-optimal algorithm for reinforcement learning in Markov decision processes in which there is a natural metric on the state space that allows t...
Sham Kakade, Michael J. Kearns, John Langford
120
Voted
IJON
2002
74views more  IJON 2002»
15 years 3 months ago
Optimal spontaneous activity in neural network modeling
We consider the origin of the high-dimensional input space as a variable which can be optimized before or during neuronal learning. This set of variables acts as a translation on ...
Daniel Remondini, Nathan Intrator, Gastone C. Cast...
IJON
2007
85views more  IJON 2007»
15 years 4 months ago
Hierarchical dynamical models of motor function
Hierarchical models of motor function are described in which the motor system encodes a hierarchy of dynamical motor primitives. The models are based on continuous attractor neura...
Simon M. Stringer, Edmund T. Rolls
FLAIRS
2004
15 years 5 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
131
Voted
CORR
2006
Springer
101views Education» more  CORR 2006»
15 years 4 months ago
Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot
We address the problem of autonomously learning controllers for visioncapable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for genera...
Viktor Zhumatiy, Faustino J. Gomez, Marcus Hutter,...