Papers › Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.
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Code
Syntology Ran 7 of 29 code samples harvested from 6 repositories linked to this paper; 22 have no recorded run. Of those that ran: 1 ran · violated contract; 4 ran · our draft was wrong; 1 ran · fixture could not drive it; 1 ran with no contract checked.
By repository: official repository: 5 samples from 1 repository, 4 ran; community (archive-listed): 23 samples from 5 repositories, 2 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
23 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
29 samples harvested; 7 ran; 0 honoured the contract we drafted; 22 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 1 of the 29 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 6 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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