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ICML
2002
IEEE
16 years 5 months ago
Hierarchically Optimal Average Reward Reinforcement Learning
Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ICALT
2005
IEEE
15 years 10 months ago
Building Repositories of Learning Objects in Specialized Domains: The Chasqui Approach
In this paper we describe the Chasqui approach to the construction of repositories of learning objects (LO) in specific knowledge areas. This approach is the result of our experie...
José Luis Sierra, Alfredo Fernández-...
COLT
2006
Springer
15 years 8 months ago
A Randomized Online Learning Algorithm for Better Variance Control
We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
Jean-Yves Audibert
ICML
2010
IEEE
15 years 5 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...
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 4 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh