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ICML
2005
IEEE
16 years 7 months ago
Dynamic preferences in multi-criteria reinforcement learning
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Sriraam Natarajan, Prasad Tadepalli
IDEAL
2003
Springer
16 years 2 days ago
On Hadamard-Type Output Coding in Multiclass Learning
The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, co...
Aijun Zhang, Zhi-Li Wu, Chun Hung Li, Kai-Tai Fang
IJCAI
2007
15 years 8 months ago
Learning to Count by Think Aloud Imitation
Although necessary, learning to discover new solutions is often long and difficult, even for supposedly simple tasks such as counting. On the other hand, learning by imitation pr...
Laurent Orseau
FLAIRS
1998
15 years 8 months ago
Learning to Race: Experiments with a Simulated Race Car
Our focus is on designing adaptable agents for highly dynamic environments. Wehave implementeda reinforcement learning architecture as the reactive componentof a twolayer control ...
Larry D. Pyeatt, Adele E. Howe
ML
1998
ACM
220views Machine Learning» more  ML 1998»
15 years 6 months ago
Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments
Abstract. Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all...
Toshiharu Sugawara, Victor R. Lesser