{"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/back-to-basics-benchmarking-canonical","title":"Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari","arxiv_id":"1802.08842","date":"2018-02-24","proceeding":null,"authors":["Patryk Chrabaszcz","Ilya Loshchilov","Frank Hutter"],"abstract":"Evolution Strategies (ES) have recently been demonstrated to be a viable\nalternative to reinforcement learning (RL) algorithms on a set of challenging\ndeep RL problems, including Atari games and MuJoCo humanoid locomotion\nbenchmarks. While the ES algorithms in that work belonged to the specialized\nclass of natural evolution strategies (which resemble approximate gradient RL\nalgorithms, such as REINFORCE), we demonstrate that even a very basic canonical\nES algorithm can achieve the same or even better performance. This success of a\nbasic ES algorithm suggests that the state-of-the-art can be advanced further\nby integrating the many advances made in the field of ES in the last decades.\n  We also demonstrate qualitatively that ES algorithms have very different\nperformance characteristics than traditional RL algorithms: on some games, they\nlearn to exploit the environment and perform much better while on others they\ncan get stuck in suboptimal local minima. Combining their strengths with those\nof traditional RL algorithms is therefore likely to lead to new advances in the\nstate of the art.","url_abs":"http://arxiv.org/abs/1802.08842v1","url_pdf":"http://arxiv.org/pdf/1802.08842v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"back-to-basics-benchmarking-canonical","repo_url":"https://github.com/PatrykChrabaszcz/Canonical_ES_Atari","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"mujoco","task_name":"MuJoCo"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08842","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}