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AAAI
2006
13 years 11 months ago
Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning
Reinforcement learning problems are commonly tackled with temporal difference methods, which attempt to estimate the agent's optimal value function. In most real-world proble...
Shimon Whiteson, Peter Stone
FLAIRS
2003
13 years 11 months ago
Learning from Reinforcement and Advice Using Composite Reward Functions
1 Reinforcement learning has become a widely used methodology for creating intelligent agents in a wide range of applications. However, its performance deteriorates in tasks with s...
Vinay N. Papudesi, Manfred Huber
ICML
2010
IEEE
13 years 11 months ago
Nonparametric Return Distribution Approximation for Reinforcement Learning
Standard Reinforcement Learning (RL) aims to optimize decision-making rules in terms of the expected return. However, especially for risk-management purposes, other criteria such ...
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashim...
MLDM
2005
Springer
14 years 3 months ago
Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures
This paper uses a set of 3D geometric measures with the purpose of characterizing lung nodules as malignant or benign. Based on a sample of 36 nodules, 29 benign and 7 malignant, t...
Aristófanes Corrêa Silva, Valdeci Rib...
EUROCAST
2007
Springer
182views Hardware» more  EUROCAST 2007»
14 years 4 months ago
A k-NN Based Perception Scheme for Reinforcement Learning
Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
José Antonio Martin H., Javier de Lope Asia...