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ICML
2009
IEEE
16 years 4 months ago
Constraint relaxation in approximate linear programs
Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...
Marek Petrik, Shlomo Zilberstein
ICML
2004
IEEE
16 years 4 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
ICML
2003
IEEE
16 years 4 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
ICML
2003
IEEE
16 years 4 months ago
TD(0) Converges Provably Faster than the Residual Gradient Algorithm
In Reinforcement Learning (RL) there has been some experimental evidence that the residual gradient algorithm converges slower than the TD(0) algorithm. In this paper, we use the ...
Ralf Schoknecht, Artur Merke
ISCAS
2006
IEEE
103views Hardware» more  ISCAS 2006»
15 years 10 months ago
Towards autonomous adaptive behavior in a bio-inspired CNN-controlled robot
— This paper describes a general approach for the unsupervised learning of behaviors in a behavior-based robot. The key idea is to formalize a behavior produced by a Motor Map dr...
Paolo Arena, Luigi Fortuna, Mattia Frasca, Luca Pa...