Editing Reinforcement learning (section)

=== Adversarial deep reinforcement learning ===
Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned policies. In this research area some studies initially showed that reinforcement learning policies are susceptible to imperceptible adversarial manipulations.<ref>{{cite journal |last1= Goodfellow|first1=Ian  |last2=Shlens |first2= Jonathan|last3=Szegedy|first3=Christian|title= Explaining and Harnessing Adversarial Examples |journal= International Conference on Learning Representations |date= 2015 |arxiv=1412.6572 }}</ref><ref>{{cite book |last1= Behzadan|first1=Vahid  |last2=Munir |first2= Arslan|title=Machine Learning and Data Mining in Pattern Recognition |chapter=Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks |series=Lecture Notes in Computer Science |date= 2017 |volume=10358 |pages=262–275 |doi=10.1007/978-3-319-62416-7_19 |arxiv=1701.04143|isbn=978-3-319-62415-0 |s2cid=1562290 }}</ref><ref>{{Cite book |last1=Huang |first1=Sandy |last2=Papernot |first2=Nicolas |last3=Goodfellow |first3=Ian |last4=Duan |first4=Yan |last5=Abbeel |first5=Pieter |url=http://worldcat.org/oclc/1106256905 |title=Adversarial Attacks on Neural Network Policies |date=2017-02-07 |oclc=1106256905}}</ref> While some methods have been proposed to overcome these susceptibilities, in the most recent studies it has been shown that these proposed solutions are far from providing an accurate representation of current vulnerabilities of deep reinforcement learning policies.<ref>{{cite journal |last1=Korkmaz |first1=Ezgi |date=2022 |title=Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs. |journal=Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) |volume=36 |issue=7 |pages=7229–7238 |doi=10.1609/aaai.v36i7.20684 |arxiv=2112.09025|s2cid=245219157 |doi-access=free }}</ref>