{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:32:22Z","timestamp":1723015942934},"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":[[2019,8]]},"abstract":"<jats:p>Effective options can make reinforcement learning easier by enhancing an agent's ability to both explore in a targeted manner and plan further into the future. However, learning an appropriate model of an option's dynamics in hard, requiring estimating a highly parameterized probability distribution. This paper introduces and motivates the Expected-Length Model (ELM) for options, an alternate model for transition dynamics. We prove ELM is a (biased) estimator of the traditional Multi-Time Model (MTM), but provide a non-vacuous bound on their deviation. We further prove that, in stochastic shortest path problems, ELM induces a value function that is sufficiently similar to the one induced by MTM, and is thus capable of supporting near-optimal behavior. We explore the practical utility of this option model experimentally, finding consistent support for the thesis that ELM is a suitable replacement for MTM. In some cases, we find ELM leads to more sample efficient learning, especially when options are arranged in a hierarchy.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/270","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"1951-1958","source":"Crossref","is-referenced-by-count":0,"title":["The Expected-Length Model of Options"],"prefix":"10.24963","author":[{"given":"David","family":"Abel","sequence":"first","affiliation":[{"name":"Brown University"}]},{"given":"John","family":"Winder","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County"}]},{"given":"Marie","family":"desJardins","sequence":"additional","affiliation":[{"name":"Simmons University"}]},{"given":"Michael","family":"Littman","sequence":"additional","affiliation":[{"name":"Brown University"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:48:00Z","timestamp":1564300080000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/270"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/270","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}