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» Utile Distinctions for Relational Reinforcement Learning
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IJCAI
2007
15 years 1 months ago
Utile Distinctions for Relational Reinforcement Learning
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
William Dabney, Amy McGovern
118
Voted
ICML
2007
IEEE
16 years 1 months ago
Cross-domain transfer for reinforcement learning
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcemen...
Matthew E. Taylor, Peter Stone
ICML
2004
IEEE
16 years 1 months ago
Using relative novelty to identify useful temporal abstractions in reinforcement learning
lative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning ?Ozg?ur S?im?sek ozgur@cs.umass.edu Andrew G. Barto barto@cs.umass.edu Department of Computer Scie...
Özgür Simsek, Andrew G. Barto
ILP
2007
Springer
15 years 6 months ago
Building Relational World Models for Reinforcement Learning
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
KES
1998
Springer
15 years 4 months ago
An acquisition of the relation between vision and action using self-organizing map and reinforcement learning
An agent must acquire internal representation appropriate for its task, environment, sensors. As a learning algorithm, reinforcement learning is often utilized to acquire the rela...
Kazunori Terada, Hideaki Takeda, Toyoaki Nishida