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ICML
2007
IEEE
16 years 6 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
CSL
2010
Springer
15 years 6 months ago
Evaluation of a hierarchical reinforcement learning spoken dialogue system
We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents. The dialogue strategies were learnt in a simulated environment a...
Heriberto Cuayáhuitl, Steve Renals, Oliver ...
DATE
2004
IEEE
145views Hardware» more  DATE 2004»
15 years 9 months ago
Hierarchical Adaptive Dynamic Power Management
Dynamic power management aims at extending battery life by switching devices to lower-power modes when there is a reduced demand for service. Static power management strategies can...
Zhiyuan Ren, Bruce H. Krogh, Radu Marculescu
ICML
2007
IEEE
16 years 6 months ago
Learning state-action basis functions for hierarchical MDPs
This paper introduces a new approach to actionvalue function approximation by learning basis functions from a spectral decomposition of the state-action manifold. This paper exten...
Sarah Osentoski, Sridhar Mahadevan
FLAIRS
2004
15 years 7 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