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ICML
2007
IEEE
16 years 7 months ago
Learning state-action basis functions for hierarchical MDPs
This paper introduces a new approach to actionvalue function approximation by learning basis functions from a spectral decomposition of the state-action manifold. This paper exten...
Sarah Osentoski, Sridhar Mahadevan
CEC
2009
IEEE
16 years 1 months ago
How robot morphology and training order affect the learning of multiple behaviors
— Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One ...
Joshua S. Auerbach, Josh C. Bongard
ATAL
2007
Springer
16 years 29 days ago
Transfer via inter-task mappings in policy search reinforcement learning
The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have f...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
ECML
2003
Springer
15 years 12 months ago
Pairwise Preference Learning and Ranking
We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of exampl...
Johannes Fürnkranz, Eyke Hüllermeier
ILP
2003
Springer
15 years 12 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