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ICML
2004
IEEE
14 years 10 months ago
Bellman goes relational
Motivated by the interest in relational reinforcement learning, we introduce a novel relational Bellman update operator called ReBel. It employs a constraint logic programming lan...
Kristian Kersting, Martijn Van Otterlo, Luc De Rae...
CORR
2010
Springer
152views Education» more  CORR 2010»
13 years 10 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
ECML
2004
Springer
14 years 3 months ago
Model Approximation for HEXQ Hierarchical Reinforcement Learning
HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy repthe problem at different levels of abstraction. In ...
Bernhard Hengst
COGSR
2011
77views more  COGSR 2011»
13 years 4 months ago
Learning to use episodic memory
This paper brings together work in modeling episodic memory and reinforcement learning. We demonstrate that is possible to learn to use episodic memory retrievals while simultaneo...
Nicholas A. Gorski, John E. Laird
NIPS
2004
13 years 11 months ago
Brain Inspired Reinforcement Learning
Successful application of reinforcement learning algorithms often involves considerable hand-crafting of the necessary non-linear features to reduce the complexity of the value fu...
François Rivest, Yoshua Bengio, John Kalask...