582 search results - page 10 / 117 » Gaussian Processes in Reinforcement Learning |
NIPS
2008
13 years 11 months ago
2008
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
ICML
2004
IEEE
14 years 10 months ago
2004
IEEE
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...
DSMML
2004
Springer
14 years 3 months ago
2004
Springer
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...
ROBOCUP
2007
Springer
14 years 3 months ago
2007
Springer
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
NIPS
2004
13 years 11 months ago
2004
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...