Editing Q-learning (section)

=== Others ===
Delayed Q-learning is an alternative implementation of the online ''Q''-learning algorithm, with [[Probably approximately correct learning|probably approximately correct (PAC) learning]].<ref>{{Cite journal |last1=Strehl |first1=Alexander L. |last2=Li |first2=Lihong |last3=Wiewiora |first3=Eric |last4=Langford |first4=John |last5=Littman |first5=Michael L. |year=2006 |title=Pac model-free reinforcement learning |url=https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/published-14.pdf |journal=Proc. 22nd ICML |pages=881–888}}</ref>

Greedy GQ is a variant of ''Q''-learning to use in combination with (linear) function approximation.<ref>{{cite web |first1=Hamid |last1=Maei |first2=Csaba |last2=Szepesvári |first3=Shalabh |last3=Bhatnagar |first4=Richard |last4=Sutton |url=https://webdocs.cs.ualberta.ca/~sutton/papers/MSBS-10.pdf |title=Toward off-policy learning control with function approximation in Proceedings of the 27th International Conference on Machine Learning |pages=719–726 |year=2010 |access-date=2016-01-25 |archive-url=https://web.archive.org/web/20120908050052/http://webdocs.cs.ualberta.ca/~sutton/papers/MSBS-10.pdf |archive-date=2012-09-08 |url-status=dead }}</ref> The advantage of Greedy GQ is that convergence is guaranteed even when function approximation is used to estimate the action values.

Distributional Q-learning is a variant of ''Q''-learning which seeks to model the distribution of returns rather than the expected return of each action. It has been observed to facilitate estimate by deep neural networks and can enable alternative control methods, such as risk-sensitive control.<ref>{{cite journal |last1=Hessel |first1=Matteo |last2=Modayil |first2=Joseph |last3=van Hasselt |first3=Hado |last4=Schaul |first4=Tom |last5=Ostrovski |first5=Georg |last6=Dabney |first6=Will |last7=Horgan |first7=Dan |last8=Piot |first8=Bilal |last9=Azar |first9=Mohammad |last10=Silver |first10=David |title=Rainbow: Combining Improvements in Deep Reinforcement Learning |journal=Proceedings of the AAAI Conference on Artificial Intelligence |date=February 2018 |volume=32 |doi=10.1609/aaai.v32i1.11796 |arxiv=1710.02298 |s2cid=19135734 }}</ref>