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NN
2010
Springer
187views Neural Networks» more  NN 2010»
14 years 4 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...
GECCO
2010
Springer
239views Optimization» more  GECCO 2010»
15 years 1 months ago
Benchmarking SPSA on BBOB-2010 noiseless function testbed
This paper presents the result for Simultaneous Perturbation Stochastic Approximation (SPSA) on the BBOB 2010 noiseless testbed. SPSA is a stochastic gradient approximation strate...
Steffen Finck, Hans-Georg Beyer
AAAI
2006
14 years 11 months ago
Functional Value Iteration for Decision-Theoretic Planning with General Utility Functions
We study how to find plans that maximize the expected total utility for a given MDP, a planning objective that is important for decision making in high-stakes domains. The optimal...
Yaxin Liu, Sven Koenig
RSCTC
2004
Springer
144views Fuzzy Logic» more  RSCTC 2004»
15 years 3 months ago
Approximation Spaces and Information Granulation
Abstract. In this paper, we discuss approximation spaces in a granular computing framework. Such approximation spaces generalise the approaches to concept approximation existing in...
Andrzej Skowron, Roman W. Swiniarski, Piotr Synak
GECCO
2006
Springer
146views Optimization» more  GECCO 2006»
15 years 1 months ago
Fitness function for finding out robust solutions on time-varying functions
Evolutionary Computations in dynamic/uncertain environments have attracted much attention. Studies regarding this research subjects can be classified into four categories: Noise, ...
Hisashi Handa