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» Reinforcement Learning: An Introduction
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GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
14 years 1 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
ATAL
2008
Springer
13 years 10 months ago
Sequential decision making in repeated coalition formation under uncertainty
The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learnin...
Georgios Chalkiadakis, Craig Boutilier
ICRA
2009
IEEE
139views Robotics» more  ICRA 2009»
14 years 2 months ago
Transfer of knowledge for a climbing Virtual Human: A reinforcement learning approach
— In the reinforcement learning literature, transfer is the capability to reuse on a new problem what has been learnt from previous experiences on similar problems. Adapting tran...
Benoit Libeau, Alain Micaelli, Olivier Sigaud
ICCBR
2005
Springer
14 years 1 months ago
CBR for State Value Function Approximation in Reinforcement Learning
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
Thomas Gabel, Martin A. Riedmiller
JDCTA
2010
160views more  JDCTA 2010»
13 years 2 months ago
Learning and Decision Making in Human During a Game of Matching Pennies
To gain insights into the neural basis of such adaptive decision-making processes, we investigated the nature of learning process in humans playing a competitive game with binary ...
Jianfeng Hu, Xiaofeng Li, Jinghai Yin