{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:35:51Z","timestamp":1781109351114,"version":"3.54.1"},"reference-count":0,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,1]]},"abstract":"<p>This article introduces a model-based reinforcement learning (RL) approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical on-line optimization of control action sequences. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, Particle Swarm Optimization (PSO) is applied to search for optimal solutions. This Particle Swarm Optimization Policy (PSO-P) is effective for high dimensional state spaces and does not require a priori assumptions about adequate policy representations. Furthermore, by translating RL problems into optimization tasks, the rich collection of real-world inspired RL benchmarks is made available for benchmarking numerical optimization techniques. The effectiveness of PSO-P is demonstrated on the two standard benchmarks: mountain car and cart pole.<\/p>","DOI":"10.4018\/ijsir.2016070102","type":"journal-article","created":{"date-parts":[[2016,5,23]],"date-time":"2016-05-23T21:21:21Z","timestamp":1464038481000},"page":"23-42","source":"Crossref","is-referenced-by-count":21,"title":["Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces"],"prefix":"10.4018","volume":"7","author":[{"given":"Daniel","family":"Hein","sequence":"first","affiliation":[{"name":"Technische, Universit\u00e4t M\u00fcnchen, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Hentschel","sequence":"additional","affiliation":[{"name":"Siemens AG, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas A.","family":"Runkler","sequence":"additional","affiliation":[{"name":"Siemens AG, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Udluft","sequence":"additional","affiliation":[{"name":"Siemens AG, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","container-title":["International Journal of Swarm Intelligence Research"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=153692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T10:09:59Z","timestamp":1654078199000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSIR.2016070102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2016,7,1]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijsir.2016070102","relation":{},"ISSN":["1947-9263","1947-9271"],"issn-type":[{"value":"1947-9263","type":"print"},{"value":"1947-9271","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,1]]}}}