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ESANN
2006
15 years 2 months ago
Reducing policy degradation in neuro-dynamic programming
We focus on neuro-dynamic programming methods to learn state-action value functions and outline some of the inherent problems to be faced, when performing reinforcement learning in...
Thomas Gabel, Martin Riedmiller
FSTTCS
2008
Springer
15 years 1 months ago
Analyzing the Implicit Computational Complexity of object-oriented programs
A sup-interpretation is a tool which provides upper bounds on the size of the values computed by the function symbols of a program. Sup-interpretations have shown their interest to...
Jean-Yves Marion, Romain Péchoux
119
Voted
GECCO
2009
Springer
109views Optimization» more  GECCO 2009»
15 years 7 months ago
Canonical representation genetic programming
Search spaces sampled by the process of Genetic Programming often consist of programs which can represent a function in many different ways. Thus, when the space is examined it i...
John R. Woodward, Ruibin Bai
154
Voted
WCFLP
2005
ACM
15 years 6 months ago
Lightweight program specialization via dynamic slicing
Program slicing is a well-known technique that extracts from a program those statements which are relevant to a particular criterion. While static slicing does not consider any in...
Claudio Ochoa, Josep Silva, Germán Vidal
ISIPTA
2003
IEEE
117views Mathematics» more  ISIPTA 2003»
15 years 6 months ago
Dynamic Programming for Discrete-Time Systems with Uncertain Gain
We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is des...
Gert de Cooman, Matthias C. M. Troffaes