Editing Reinforcement learning (section)

=== Fuzzy reinforcement learning ===
By introducing [[Fuzzy control system|fuzzy inference]] in reinforcement learning,<ref>{{Cite book |last=Berenji |first=H.R. |title=Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference |chapter=Fuzzy Q-learning: A new approach for fuzzy dynamic programming |date=1994 |chapter-url=https://ieeexplore.ieee.org/document/343737 |location=Orlando, FL, USA |publisher=IEEE |pages=486–491 |doi=10.1109/FUZZY.1994.343737|isbn=0-7803-1896-X |s2cid=56694947 }}</ref> approximating the state-action value function with [[fuzzy rule]]s in continuous space becomes possible. The IF - THEN form of fuzzy rules make this approach suitable for expressing the results in a form close to natural language. Extending FRL with Fuzzy Rule Interpolation<ref>{{Cite book |last=Vincze |first=David |title=2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) |date=2017 |chapter=Fuzzy rule interpolation and reinforcement learning |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> allows the use of reduced size sparse fuzzy rule-bases to emphasize cardinal rules (most important state-action values).