Reinforcement Learning

Ke-Lin Du () and M. N. S. Swamy
Additional contact information
Ke-Lin Du: Concordia University, Department of Electrical and Computer Engineering
M. N. S. Swamy: Concordia University, Department of Electrical and Computer Engineering

Chapter Chapter 17 in Neural Networks and Statistical Learning, 2019, pp 503-523 from Springer

Abstract: Abstract One of the primary goals of AI is to produce fully autonomous agents that learn optimal behaviors through trial and error by interacting with their environments. The reinforcement learning paradigm is essentially learning through interaction. It has its root in behaviorist psychology. Reinforcement learning is influenced by optimal control, which is underpinned by mathematical dynamic programming formalism. This chapter deals with reinforcement learning.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4471-7452-3_17

Ordering information: This item can be ordered from
http://www.springer.com/9781447174523

DOI: 10.1007/978-1-4471-7452-3_17

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().