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| 2018 || {{dts|February 20}} || Donation || OpenAI announces changes in donors and advisors. New donors are: {{W|Jed McCaleb}}, {{W|Gabe Newell}}, {{W|Michael Seibel}}, {{W|Jaan Tallinn}}, and {{W|Ashton Eaton}} and {{W|Brianne Theisen-Eaton}}. {{W|Reid Hoffman}} is "significantly increasing his contribution". Pieter Abbeel (previously at OpenAI), {{W|Julia Galef}}, and Maran Nelson become advisors. {{W|Elon Musk}} departs the board but remains as a donor and advisor.<ref>{{cite web |url=https://blog.OpenAI.com/OpenAI-supporters/ |publisher=OpenAI Blog |title=OpenAI Supporters |date=February 21, 2018 |accessdate=March 1, 2018}}</ref><ref name="musk-leaves">{{cite web |url=https://www.theverge.com/2018/2/21/17036214/elon-musk-OpenAI-ai-safety-leaves-board |date=February 21, 2018 |publisher=The Verge |title=Elon Musk leaves board of AI safety group to avoid conflict of interest with Tesla |accessdate=March 2, 2018}}</ref>
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| 2018 || {{dts|February 26}} || Milestone release || OpenAI releases eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for OpenAI research over the previous year. These environments were to train models which work on physical robots.<ref>{{cite web |title=Ingredients for Robotics Research |url=https://openai.com/blog/ingredients-for-robotics-research/ |website=openai.com |accessdate=5 April 2020}}</ref>
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| 2018 || {{dts|February 26}} || Publication || "Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research" is first submitted to the {{w|ArXiv}}. The paper introduces a suite of challenging continuous control tasks based on currently existing robotics hardware, and presents a set of concrete research ideas for improving {{w|reinforcement learning}} algorithms.<ref>{{cite web |title=Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research |url=https://arxiv.org/abs/1802.09464 |website=arxiv.org |accessdate=26 March 2020}}</ref>
| 2018 || {{Dts|April 5}}{{snd}}June 5 || Event host || The OpenAI Retro Contest takes place.<ref>{{cite web |url=https://contest.OpenAI.com/ |title=OpenAI Retro Contest |publisher=OpenAI |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://blog.OpenAI.com/retro-contest/ |publisher=OpenAI Blog |title=Retro Contest |date=April 13, 2018 |accessdate=May 5, 2018}}</ref> As a result of the release of the Gym Retro library, OpenAI's Universe become deprecated.<ref>{{cite web |url=https://github.com/OpenAI/universe/commit/cc9ce6ec241821bfb0f3b85dd455bd36e4ee7a8c |publisher=GitHub |title=OpenAI/universe |accessdate=May 5, 2018}}</ref>
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| 2018 || {{dts|April 9}} || Commitment || OpenAI releases a charter. The charter says in part stating that OpenAI the organization commits to stop competing with a value-aligned and safety-conscious project that comes close to building artificial general intelligence, and also that OpenAI expects to reduce its traditional publishing in the future due to safety concerns.<ref>{{cite web |url=https://blog.OpenAI.com/OpenAI-charter/ |publisher=OpenAI Blog |title=OpenAI Charter |date=April 9, 2018 |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://www.lesswrong.com/posts/e5mFQGMc7JpechJak/OpenAI-charter |title=OpenAI charter |accessdate=May 5, 2018 |date=April 9, 2018 |author=wunan |publisher=[[wikipedia:LessWrong|LessWrong]]}}</ref><ref>{{cite web |url=https://www.reddit.com/r/MachineLearning/comments/8azk2n/d_OpenAI_charter/ |publisher=reddit |title=[D] OpenAI Charter • r/MachineLearning |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://news.ycombinator.com/item?id=16794194 |title=OpenAI Charter |website=Hacker News |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://thenextweb.com/artificial-intelligence/2018/04/10/the-ai-company-elon-musk-co-founded-is-trying-to-create-sentient-machines/ |title=The AI company Elon Musk co-founded intends to create machines with real intelligence |publisher=The Next Web |date=April 10, 2018 |author=Tristan Greene |accessdate=May 5, 2018}}</ref>
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| 2018 || {{dts|April 10}} || Publication || "Gotta Learn Fast: A New Benchmark for Generalization in RL", a paper on {{w|reinforcement learning}}, is first submitted to the {{w|ArXiv}}. The report presents a new {{w|reinforcement learning}} benchmark intended to measure the performance of transfer learning and few-shot learning algorithms in the reinforcement learning domain.<ref>{{cite web |last1=Nichol |first1=Alex |last2=Pfau |first2=Vicki |last3=Hesse |first3=Christopher |last4=Klimov |first4=Oleg |last5=Schulman |first5=John |title=Gotta Learn Fast: A New Benchmark for Generalization in RL |url=https://arxiv.org/abs/1804.03720 |website=arxiv.org |accessdate=26 March 2020}}</ref>
| 2018|| {{dts|June 2}} || Publication || OpenAI publishes "GamePad: A Learning Environment for Theorem Proving" in {{w|arXiv}}. The paper introduces a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.<ref>{{cite web |last1=Huang |first1=Daniel |last2=Dhariwal |first2=Prafulla |last3=Song |first3=Dawn |last4=Sutskever |first4=Ilya |title=GamePad: A Learning Environment for Theorem Proving |url=https://arxiv.org/abs/1806.00608 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|June 11}} || Milestone release || OpenAI announces having obtained significant results on a suite of diverse language tasks with a scalable, task-agnostic system, which uses a combination of transformers and unsupervised pre-training.<ref>{{cite web |title=Improving Language Understanding with Unsupervised Learning |url=https://openai.com/blog/language-unsupervised/ |website=openai.com |accessdate=4 April 2020}}</ref>
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| 2018 || {{dts|June 17}} || Publication || OpenAI publishes paper on learning policy representations in multiagent systems. The paper proposes a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data.<ref>{{cite web |title=Learning Policy Representations in Multiagent Systems |url=https://arxiv.org/abs/1806.06464 |website=arxiv.org |accessdate=26 March 2020}}</ref>
