Abstract
Definition of Q-learning.
Definition
Q-learning is a form of temporal difference learning. As such, it is a model-free reinforcement learning method combining elements of dynamic programming with Monte Carlo estimation. Due in part to Watkins’ (1989) proof that it converges to the optimal value function, Q-learning is among the most commonly used and well-known reinforcement learning algorithms.
Cross-References
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Watkins CJCH (1989) Learning from delayed rewards. PhD thesis. King’s College, Cambridge
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Stone, P. (2017). Q-Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_689
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_689
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering