Name: How AI trained to beat Atari games could impact robotics and drug design
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How AI trained to beat Atari games could impact robotics and drug design

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×Please wait...Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! .In 2018, Uber AI Labs introduced Go-Explore, a family of algorithms that , a commonly accepted challenge. Last year, Go-Explore was .Now researchers from OpenAI and Uber AI Labs say Go-Explore has solved all previously unsolved games in the from the Arcade Learning Environment, a collection of more than 50 games, including Pitfall and Pong. Go-Explore also quadruples the state-of-the-art score performance on Montezuma’s Revenge.Training agents to navigate complex environments has long been considered a challenge for reinforcement learning. Success in these areas has accounted for some major machine learning milestones, like or beating human champions.Researchers envision recent Go-Explore advances being applied to language models but also used for drug design and robotics trained to navigate the world safely. In simulations, a robotic arm was able to successfully pick up an object and put it on one of four shelves, two of which are behind doors with latches. The ability to complete this transfer, they say, proves the policy approach is not simply leveraging the ability to restore a previously held state in a reinforcement learning environment, but a “function of its overall design.”MetaBeat 2022MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.“The insights presented in this work extend broadly; the simple decomposition of remembering previously found states, returning to them, and then exploring from them appears to be especially powerful, suggesting it may be a fundamental feature of learning in general. Harnessing these insights, either within or outside of the context of Go-Explore, may be essential to improve our ability to create generally intelligent agents,” reads a paper on the research .Researchers theorize that part of the problem is that agents in reinforcement learning environments forget how to get to places they have previously been (known as detachment) and generally fail to return to a state before exploring from it (known as derailment).“To avoid detachment, Go-Explore builds an ‘archive’ of the different states it has visited in the environment, thus ensuring that states cannot be forgotten. Starting from an archive containing only the initial state, it builds this archive iteratively,” the paper reads. “By first returning before exploring, Go-Explore avoids derailment by minimizing exploration when returning (thus minimizing failure to return) after which it can focus purely on exploration.”Last year Jeff Clune, who cofounded Uber AI Labs in 2017 before moving to OpenAI last year, . Solving this problem, he said at the time, .In other recent news, OpenAI shared about multimodal model CLIPS this week, and the AI Index, compiled in part by former OpenAI policy director Jack Clark, was . The annual index chronicles AI performance progress, as well as trends in startup investment, education, diversity, and policy.VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.Join metaverse thought leaders in San Francisco on October 4 to learn how metaverse technology will transform the way all industries communicate and do business.Did you miss a session from Transform 2022? Head over to the on-demand library for all of our featured sessions.We may collect cookies and other personal information from your interaction with ourwebsite.For more information on the categories of personal information we collect and the purposes we usethem for,please view our

×Please wait...Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! .In 2018, Uber AI Labs introduced Go-Explore, a family of algorithms that , a commonly accepted challenge. Last year, Go-Explore was .Now researchers from OpenAI and Uber AI Labs say Go-Explore has solved all previously unsolved games in the from the Arcade Learning Environment, a collection of more than 50 games, including Pitfall and Pong. Go-Explore also quadruples the state-of-the-art score performance on Montezuma’s Revenge.Training agents to navigate complex...

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How AI trained to beat Atari games could impact robotics and drug design

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