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» Coarticulation in Markov Decision Processes
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CDC
2009
IEEE
147views Control Systems» more  CDC 2009»
13 years 6 months ago
A probabilistic approach for control of a stochastic system from LTL specifications
We consider the problem of controlling a continuous-time linear stochastic system from a specification given as a Linear Temporal Logic (LTL) formula over a set of linear predicate...
Morteza Lahijanian, Sean B. Andersson, Calin Belta
ICMCS
2009
IEEE
149views Multimedia» more  ICMCS 2009»
13 years 6 months ago
A multi-agent framework for a hybrid dialog management system
The importance of dialog management systems has increased in recent years. Dialog systems are created for domain specific applications, so that a high demand for a flexible dialog...
Stefan Schwärzler, Joachim Schenk, Günth...
JMLR
2010
189views more  JMLR 2010»
13 years 3 months ago
Adaptive Step-size Policy Gradients with Average Reward Metric
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
NN
2010
Springer
187views Neural Networks» more  NN 2010»
13 years 3 months ago
Efficient exploration through active learning for value function approximation in reinforcement learning
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
ICML
2007
IEEE
14 years 9 months ago
Constructing basis functions from directed graphs for value function approximation
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
Jeffrey Johns, Sridhar Mahadevan