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An example of these conditions is maneuver sports. Movement\u2010type simulations, viz., the esports game Street Fighter (SF), recapitulate the complex multicharacter interactions and, concurrently, generate the millisecond\u2010level control challenges of human athletes. Herein, the physical and mental signatures of the SF agent (it is called SF R2) are controlled by utilizing a previously unreported model\u2010free, natural, deep reinforcement learning algorithm \u201cDecay\u2010based Phase\u2010change memristive character\u2010type Proximal Policy Optimization\u201d (DP\u2010PPO) through an assemblage of hybrid case\u2010type training processes; and an integrated training configuration for time\u2010trial evaluations, as well as competitions with a world's best SF player, is developed. A short length of time utilized by the SF R2 to defeat the opponent and, simultaneously, maintaining a good health level is achieved, as well as excellent handling of imperfect information settings. Training studies reveal a moderate maneuver etiquette in the SF R2, along with rapid, effective head\u2010to\u2010head competitions with one of the world's best SF player. This paves the way for achieving a broadly applicable training scheme, capable of quickly controlling complicated\u2010movement systems in fields where agents should observe unspecified human norms.<\/jats:p>","DOI":"10.1002\/aisy.202300335","type":"journal-article","created":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T22:51:57Z","timestamp":1693176717000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Phase\u2010Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street\u2010Fighter Players"],"prefix":"10.1002","volume":"5","author":[{"given":"Shao-Xiang","family":"Go","sequence":"first","affiliation":[{"name":"Department of Science, Mathematics and Technology and The AI Mega Centre Singapore University of Technology and Design  Singapore 487372 Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Science, Mathematics and Technology and The AI Mega Centre Singapore University of Technology and Design  Singapore 487372 Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5799-6441","authenticated-orcid":false,"given":"Desmond K.","family":"Loke","sequence":"additional","affiliation":[{"name":"Department of Science, Mathematics and Technology and The AI Mega Centre Singapore University of Technology and Design  Singapore 487372 Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,8,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1724-z"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar6404"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04357-7"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108284"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3093340"},{"key":"e_1_2_9_8_1","unstructured":"S.Gu E.Holly T.Lillicrap S.Levine presented at2017 IEEE Int. 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