{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evolution-strategies-as-a-scalable","title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","arxiv_id":"1703.03864","date":"2017-03-10","proceeding":null,"authors":["Tim Salimans","Jonathan Ho","Xi Chen","Szymon Sidor","Ilya Sutskever"],"abstract":"We explore the use of Evolution Strategies (ES), a class of black box\noptimization algorithms, as an alternative to popular MDP-based RL techniques\nsuch as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show\nthat ES is a viable solution strategy that scales extremely well with the\nnumber of CPUs available: By using a novel communication strategy based on\ncommon random numbers, our ES implementation only needs to communicate scalars,\nmaking it possible to scale to over a thousand parallel workers. This allows us\nto solve 3D humanoid walking in 10 minutes and obtain competitive results on\nmost Atari games after one hour of training. In addition, we highlight several\nadvantages of ES as a black box optimization technique: it is invariant to\naction frequency and delayed rewards, tolerant of extremely long horizons, and\ndoes not need temporal discounting or value function approximation.","url_abs":"http://arxiv.org/abs/1703.03864v2","url_pdf":"http://arxiv.org/pdf/1703.03864v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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