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GECCO
2011
Springer
12 years 10 months ago
2011
Springer
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
ATAL
2004
Springer
14 years 12 days ago
2004
Springer
This paper explores hybrid agents that use a variety of techniques to improve their performance in an environment over time. We considered, specifically, geneticlearning-parentin...
GECCO
2008
Springer
13 years 8 months ago
2008
Springer
Single and multi-step time-series predictors were evolved for forecasting minimum bidding prices in a simulated supply chain management scenario. Evolved programs were allowed to ...
AAAI
2007
13 years 9 months ago
2007
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and te...
ICONIP
2007
13 years 8 months ago
2007
The aim of the Cyber Rodent project [1] is to elucidate the origin of our reward and affective systems by building artificial agents that share the natural biological constraints...