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» Universal Reinforcement Learning
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ICML
2005
IEEE
16 years 5 months ago
Interactive learning of mappings from visual percepts to actions
We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier i...
Justus H. Piater, Sébastien Jodogne
ATAL
2009
Springer
15 years 11 months ago
Learning of coordination: exploiting sparse interactions in multiagent systems
Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limi...
Francisco S. Melo, Manuela M. Veloso
ICWL
2004
Springer
15 years 9 months ago
CUBES: Providing Flexible Learning Environment for Virtual Universities
Abstract. To enable an online virtual university, a learning environment covering the entire spectrum of the learning and management process is required. However, constructing such...
Peifeng Xiang, Yuanchun Shi, Weijun Qin, Xin Xiang
CEC
2005
IEEE
15 years 6 months ago
XCS with computed prediction in continuous multistep environments
We apply XCS with computed prediction (XCSF) to tackle multistep reinforcement learning problems involving continuous inputs. In essence we use XCSF as a method of generalized rein...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
ICML
2006
IEEE
16 years 5 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto