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| 2018 || {{Dts|November 19}} || || Partnership || OpenAI partners with {{w|DeepMind}} in a new paper that proposes a new method to train {{w|reinforcement learning}} agents in ways that enables them to surpass human performance. The paper, titled ''Reward learning from human preferences and demonstrations in Atari'', introduces a training model that combines human feedback and reward optimization to maximize the knowledge of RL agents.<ref>{{cite web |last1=Rodriguez |first1=Jesus |title=What’s New in Deep Learning Research: OpenAI and DeepMind Join Forces to Achieve Superhuman Performance in Reinforcement Learning |url=https://towardsdatascience.com/whats-new-in-deep-learning-research-OpenAI-and-deepmind-join-forces-to-achieve-superhuman-48e7d1accf85 |website=towardsdatascience.com |accessdate=29 June 2019}}</ref>
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| 2018 || {{dts|December 4}} || {{w|Reinforcement learning}} || Researh progress || OpenAI announces having discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks.<ref>{{cite web |title=How AI Training Scales |url=https://openai.com/blog/science-of-ai/ |website=openai.com |accessdate=4 April 2020}}</ref>
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| 2018 || {{Dts|December 6}} || {{w|Reinforcement learning}} || Software release || OpenAI releases CoinRun, a training environment designed to test the adaptability of reinforcement learning agents.<ref>{{cite web |title=OpenAI teaches AI teamwork by playing hide-and-seek |url=https://venturebeat.com/2019/09/17/OpenAI-and-deepmind-teach-ai-to-work-as-a-team-by-playing-hide-and-seek/ |website=venturebeat.com |accessdate=24 February 2020}}</ref><ref>{{cite web |title=OpenAI’s CoinRun tests the adaptability of reinforcement learning agents |url=https://venturebeat.com/2018/12/06/OpenAIs-coinrun-tests-the-adaptability-of-reinforcement-learning-agents/ |website=venturebeat.com |accessdate=24 February 2020}}</ref>
