{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T18:21:24Z","timestamp":1778955684210,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Customized sports training routines take into account individual physiology, fatigue, and recovery to maximize performance. Proximal Policy Optimization (PPO)-based reinforcement learning is used to adjust training intensity, duration, and rest in a simulated endurance-training environment for runners, using real-time wearable and performance data. The environment models athlete status utilizing heart rate variability, VO\u2082 max, fatigue ratings, and injury-risk indicators. PPO is trained to maximize performance gains, recovery quality, and safety over repeated sessions. Simulated policy improves performance (18.6%), injury-risk deviation (\u221222.4%), recovery compliance (91.3%), training load variability control (\u00b17.2%), reward-signal evolution convergence (+41.7%), session completion rate (94.6%), personalized adaptation score (87.5%), and fatigue index stability (94.3%). Results show that a PPO-based RL setup, specifically defined by state design, reward shaping, and multi-episode training, can provide adaptive and data-driven tailored sports training.<\/jats:p>","DOI":"10.31449\/inf.v50i8.10131","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T11:59:57Z","timestamp":1771761597000},"source":"Crossref","is-referenced-by-count":1,"title":["A Proximal Policy Optimization-Based Reinforcement Learning Framework for Real-Time Personalized Endurance Training"],"prefix":"10.31449","volume":"50","author":[{"given":"Chuanzhong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danqing","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunlong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10131\/6534","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10131\/6534","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:01:38Z","timestamp":1771840898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i8.10131","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}