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» Constructing States for Reinforcement Learning
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ESANN
2007
15 years 7 months ago
The Recurrent Control Neural Network
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
Anton Maximilian Schäfer, Steffen Udluft, Han...
SGAI
2010
Springer
15 years 4 months ago
Hierarchical Traces for Reduced NSM Memory Requirements
This paper presents work on using hierarchical long term memory to reduce the memory requirements of nearest sequence memory (NSM) learning, a previously published, instance-based ...
Torbjørn S. Dahl
INTERSPEECH
2010
15 years 27 days ago
Still talking to machines (cognitively speaking)
This overview article reviews the structure of a fully statistical spoken dialogue system (SDS), using as illustration, various systems and components built at Cambridge over the ...
Steve Young
TSMC
2008
146views more  TSMC 2008»
15 years 6 months ago
Decentralized Learning in Markov Games
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is tha...
Peter Vrancx, Katja Verbeeck, Ann Nowé
IWCLS
2007
Springer
16 years 7 days ago
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most c...
Larry Bull