{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:00:53Z","timestamp":1760709653112},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Large state and action spaces are very challenging to reinforcement learning. However, in many domains there is a set of algorithms available, which estimate the best action given a state. Hence, agents can either directly learn a performance-maximizing mapping from states to actions, or from states to algorithms. We investigate several aspects of this dilemma, showing sufficient conditions for learning over algorithms to outperform over actions for a finite number of training iterations. We present synthetic experiments to further study such systems. Finally, we propose a function approximation approach, demonstrating the effectiveness of learning over algorithms in real-time strategy games.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/377","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"2717-2723","source":"Crossref","is-referenced-by-count":7,"title":["Algorithms or Actions? A Study in Large-Scale Reinforcement Learning"],"prefix":"10.24963","author":[{"given":"Anderson Rocha","family":"Tavares","sequence":"first","affiliation":[{"name":"Computer Science Department -- Universidade Federal de Minas Gerais"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sivasubramanian","family":"Anbalagan","sequence":"additional","affiliation":[{"name":"School of Computing and Communications -- Lancaster University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leandro","family":"Soriano Marcolino","sequence":"additional","affiliation":[{"name":"School of Computing and Communications -- Lancaster University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luiz","family":"Chaimowicz","sequence":"additional","affiliation":[{"name":"Computer Science Department -- Universidade Federal de Minas Gerais"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:52:09Z","timestamp":1530769929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/377"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/377","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}