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» Graph Kernels and Gaussian Processes for Relational Reinforc...
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ILP
2003
Springer
13 years 8 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon
PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
13 years 10 months ago
Learning Preferences with Hidden Common Cause Relations
Abstract. Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic...
Kristian Kersting, Zhao Xu
DSMML
2004
Springer
13 years 9 months ago
Understanding Gaussian Process Regression Using the Equivalent Kernel
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show how to approximat...
Peter Sollich, Christopher K. I. Williams
ICML
2005
IEEE
14 years 4 months ago
Reinforcement learning with Gaussian processes
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
Yaakov Engel, Shie Mannor, Ron Meir
NIPS
2004
13 years 5 months ago
Using the Equivalent Kernel to Understand Gaussian Process Regression
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...
Peter Sollich, Christopher K. I. Williams