Integrating Experimentation and Guidance in Relational Reinforcement Learning
Abstract
Reinforcement learning, and Q-learning in particular, encounter two major problems when dealing with large state spaces. First, learning the Q-function in tabular form may be infeasible because of the excessive amount of memory needed to store the table, and because the Q-function only converges after each state has been visited multiple times.
Cite
Text
Driessens and Dzeroski. "Integrating Experimentation and Guidance in Relational Reinforcement Learning." International Conference on Machine Learning, 2002.Markdown
[Driessens and Dzeroski. "Integrating Experimentation and Guidance in Relational Reinforcement Learning." International Conference on Machine Learning, 2002.](https://mlanthology.org/icml/2002/driessens2002icml-integrating/)BibTeX
@inproceedings{driessens2002icml-integrating,
title = {{Integrating Experimentation and Guidance in Relational Reinforcement Learning}},
author = {Driessens, Kurt and Dzeroski, Saso},
booktitle = {International Conference on Machine Learning},
year = {2002},
pages = {115-122},
url = {https://mlanthology.org/icml/2002/driessens2002icml-integrating/}
}