{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:31:19Z","timestamp":1723015879670},"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":[[2017,8]]},"abstract":"<jats:p>In the context of hierarchical reinforcement learning, the idea of hierarchies of abstract machines (HAMs) is to write a partial policy as a set of hierarchical finite state machines with unspecified choice states, and use reinforcement learning to learn an optimal completion of this partial policy. Given a HAM with potentially deep hierarchical structure, there often exist many internal transitions where a machine calls another machine with the environment state unchanged. In this paper, we propose a new hierarchical reinforcement learning algorithm that discovers such internal transitions automatically, and shortcircuits them recursively in computation of Q values. The resulting HAMQ-INT algorithm outperforms the state of the art significantly on the benchmark Taxi domain and a much more complex RoboCup Keepaway domain.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/196","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"1418-1424","source":"Crossref","is-referenced-by-count":7,"title":["Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions"],"prefix":"10.24963","author":[{"given":"Aijun","family":"Bai","sequence":"first","affiliation":[{"name":"University of California at Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stuart","family":"Russell","sequence":"additional","affiliation":[{"name":"University of California at Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:52:43Z","timestamp":1501242763000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/196"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/196","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}