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SARA
2005
Springer
16 years 3 days ago
Feature-Discovering Approximate Value Iteration Methods
Sets of features in Markov decision processes can play a critical role ximately representing value and in abstracting the state space. Selection of features is crucial to the succe...
Jia-Hong Wu, Robert Givan
EUROCRYPT
2001
Springer
15 years 11 months ago
Evidence that XTR Is More Secure than Supersingular Elliptic Curve Cryptosystems
Abstract. We show that finding an efficiently computable injective homomorphism from the XTR subgroup into the group of points over GF(p2 ) of a particular type of supersingular e...
Eric R. Verheul
AIPS
2007
15 years 9 months ago
Discovering Relational Domain Features for Probabilistic Planning
In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
Jia-Hong Wu, Robert Givan
TOMACS
2010
79views more  TOMACS 2010»
15 years 1 months ago
A stochastic approximation method with max-norm projections and its applications to the Q-learning algorithm
In this paper, we develop a stochastic approximation method to solve a monotone estimation problem and use this method to enhance the empirical performance of the Q-learning algor...
Sumit Kunnumkal, Huseyin Topaloglu
LICS
2009
IEEE
16 years 1 months ago
Functional Reachability
—What is reachability in higher-order functional programs? We formulate reachability as a decision problem in the setting of the prototypical functional language PCF, and show th...
C.-H. Luke Ong, Nikos Tzevelekos