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AIPS
2006
15 years 5 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens
ATAL
2010
Springer
15 years 5 months ago
Basis function construction for hierarchical reinforcement learning
This paper introduces an approach to automatic basis function construction for Hierarchical Reinforcement Learning (HRL) tasks. We describe some considerations that arise when con...
Sarah Osentoski, Sridhar Mahadevan
PKDD
2009
Springer
144views Data Mining» more  PKDD 2009»
15 years 11 months ago
Compositional Models for Reinforcement Learning
Abstract. Innovations such as optimistic exploration, function approximation, and hierarchical decomposition have helped scale reinforcement learning to more complex environments, ...
Nicholas K. Jong, Peter Stone
KES
2007
Springer
15 years 10 months ago
Making Financial Trading by Recurrent Reinforcement Learning
In this paper we propose a financial trading system whose strategy is developed by means of an artificial neural network approach based on a recurrent reinforcement learning algo...
Francesco Bertoluzzo, Marco Corazza
ICRA
2006
IEEE
161views Robotics» more  ICRA 2006»
15 years 10 months ago
Quadruped Robot Obstacle Negotiation via Reinforcement Learning
— Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning to the proble...
Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Sin...