Editing Q-learning (section)

=== Function approximation ===
''Q''-learning can be combined with [[function approximation]].<ref>{{cite book|chapter-url={{google books |plainurl=y |id=YPjNuvrJR0MC|pp= 207-251}}|title=Reinforcement Learning: State-of-the-Art|editor-last1=Wiering|editor-first1=Marco|editor-last2=Otterlo|editor-first2=Martijn van|date=5 March 2012|publisher=Springer Science & Business Media |first=Hado van |last=Hasselt |chapter=Reinforcement Learning in Continuous State and Action Spaces |pages= 207–251 |isbn=978-3-642-27645-3}}</ref> This makes it possible to apply the algorithm to larger problems, even when the state space is continuous.

One solution is to use an (adapted) [[artificial neural network]] as a function approximator.<ref name="CACM">{{cite journal|last=Tesauro|first=Gerald|date=March 1995|title=Temporal Difference Learning and TD-Gammon|url=http://www.bkgm.com/articles/tesauro/tdl.html|journal=Communications of the ACM|volume=38|issue=3|pages=58–68|doi=10.1145/203330.203343|s2cid=8763243|access-date=2010-02-08|doi-access=free}}</ref> Another possibility is to integrate Fuzzy Rule Interpolation (FRI) and use sparse [[Fuzzy rule|fuzzy rule-bases]]<ref>{{Cite book |last=Vincze |first=David |title=2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) |chapter=Fuzzy rule interpolation and reinforcement learning |date=2017 |chapter-url=http://users.iit.uni-miskolc.hu/~vinczed/research/vinczed_sami2017_author_draft.pdf |publisher=IEEE |pages=173–178 |doi=10.1109/SAMI.2017.7880298|isbn=978-1-5090-5655-2 |s2cid=17590120 }}</ref> instead of discrete Q-tables or ANNs, which has the advantage of being a human-readable knowledge representation form. Function approximation may speed up learning in finite problems, due to the fact that the algorithm can generalize earlier experiences to previously unseen states.