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| 2018 || {{dts|October}} || Staff || Mark Chen joins OpenAI as Research Scientist.<ref>{{cite web |title=Mark Chen |url=https://www.linkedin.com/in/markchen90/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{dts|October 31}} || Software || OpenAI unveils its Random Network Distillation (RND), a prediction-based method for encouraging {{w|reinforcement learning}} agents to explore their environments through curiosity, which for the first time exceeds average human performance on videogame Montezuma’s Revenge.<ref>{{cite web |title=Reinforcement Learning with Prediction-Based Rewards |url=https://openai.com/blog/reinforcement-learning-with-prediction-based-rewards/ |website=openai.com |accessdate=5 April 2020}}</ref>
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| 2018 || {{Dts|November 1}} || Publication || OpenAI publishes research paper detailing AI able to defeat humans at the retro platformer [[w:Montezuma's Revenge (video game)|Montezuma’s Revenge]]. The top-performing iteration found 22 of the 24 rooms in the first level, and occasionally discovered all 24.<ref>{{cite web |last1=Wiggers |first1=Kyle |title=OpenAI made a system that’s better at Montezuma’s Revenge than humans |url=https://venturebeat.com/2018/11/01/OpenAI-made-a-system-thats-better-at-montezumas-revenge-than-humans/ |website=venturebeat.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Vincent |first1=James |title=New research from OpenAI uses curious AI to beat video games |url=https://www.theverge.com/2018/11/1/18051196/ai-artificial-intelligence-curiosity-OpenAI-montezumas-revenge-noisy-tv-problem |website=theverge.com |accessdate=15 June 2019}}</ref>
