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| 2016 || {{dts|May 25}} || Safety || Publication || "Adversarial Training Methods for Semi-Supervised Text Classification" is submitted to the {{w|ArXiv}}. The paper proposes a method that achieves better results on multiple benchmark semi-supervised and purely supervised tasks.<ref>{{cite web |last1=Miyato |first1=Takeru |last2=Dai |first2=Andrew M. |last3=Goodfellow |first3=Ian |title=Adversarial Training Methods for Semi-Supervised Text Classification |url=https://arxiv.org/abs/1605.07725 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2016 || {{dts|May 31}} || Generative models || Publication || "VIME: Variational Information Maximizing Exploration", a paper on generative models, is submitted to the {{w|ArXiv}}. The paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics.<ref>{{cite web |last1=Houthooft |first1=Rein |last2=Chen |first2=Xi |last3=Duan |first3=Yan |last4=Schulman |first4=John |last5=De Turck |first5=Filip |last6=Abbeel |first6=Pieter |title=VIME: Variational Information Maximizing Exploration |url=https://arxiv.org/abs/1605.09674 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2016 || {{dts|June 5}} || || Publication || "OpenAI Gym", a paper on {{w|reinforcement learning}}, is submitted to the {{w|ArXiv}}. It presents OpenAI Gym as a toolkit for reinforcement learning research.<ref>{{cite web |last1=Brockman |first1=Greg |last2=Cheung |first2=Vicki |last3=Pettersson |first3=Ludwig |last4=Schneider |first4=Jonas |last5=Schulman |first5=John |last6=Tang |first6=Jie |last7=Zaremba |first7=Wojciech |title=OpenAI Gym |url=https://arxiv.org/abs/1606.01540 |website=arxiv.org |accessdate=27 March 2020}}</ref>
