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» Percentile optimization in uncertain Markov decision process...
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ICML
2007
IEEE
9 years 9 months ago
Percentile optimization in uncertain Markov decision processes with application to efficient exploration
Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from da...
Erick Delage, Shie Mannor
AAAI
2006
8 years 9 months ago
Decision Making in Uncertain Real-World Domains Using DT-Golog
DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties associated with Markov Decisi...
Mikhail Soutchanski, Huy Pham, John Mylopoulos
ICML
2006
IEEE
9 years 9 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto
CORR
2010
Springer
112views Education» more  CORR 2010»
8 years 8 months ago
Efficient Approximation of Optimal Control for Markov Games
The success of probabilistic model checking for discrete-time Markov decision processes and continuous-time Markov chains has led to rich academic and industrial applications. The ...
Markus Rabe, Sven Schewe, Lijun Zhang
AUTOMATICA
2007
124views more  AUTOMATICA 2007»
8 years 8 months ago
Motion planning in uncertain environments with vision-like sensors
In this work we present a methodology for intelligent path planning in an uncertain environment using vision like sensors, i.e., sensors that allow the sensing of the environment ...
Suman Chakravorty, John L. Junkins
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