{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:03:21Z","timestamp":1740099801112,"version":"3.37.3"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,9]]},"DOI":"10.1109\/icca51439.2020.9264548","type":"proceedings-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T21:36:44Z","timestamp":1606772204000},"page":"1161-1166","source":"Crossref","is-referenced-by-count":1,"title":["Locomotion Control Method for Humanoid Robot Based on United Hierarchical Reinforcement Learning"],"prefix":"10.1109","author":[{"given":"Boying","family":"Liu","sequence":"first","affiliation":[]},{"given":"Lu","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Chenju","family":"Liu","sequence":"additional","affiliation":[]},{"given":"BinChen","family":"Xu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008920021246"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008928605612"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/S0960-9822(00)00367-5"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7140079"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.1999.785511"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.23919\/EETA.2019.8804490"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41192-7_9"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpsc.2018.06.010"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02465-9_43"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms8522"},{"key":"ref4","first-page":"371","article-title":"A hierarchical reinforcement learning algorithm based on heuristic reward function[C]","volume":"3","author":"yan","year":"2010","journal-title":"Proceedings of 2nd International Conference on Advanced Computer Control Shenyang"},{"key":"ref27","first-page":"485","article-title":"State of the art control of atari games using shallow reinforcement learning[C]","author":"liang","year":"2016","journal-title":"Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems"},{"key":"ref3","first-page":"3675","article-title":"Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation[C]","author":"kulkarni","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.12785\/ijcds\/040207"},{"key":"ref5","first-page":"110","article-title":"Combining hierarchical reinforcement learning and Bayesian networks for natural language generation in situated dialogue[C]","author":"dethlefs","year":"2011","journal-title":"European Workshop on Natural Language Generation Nancy"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/COASE.2010.5584599"},{"key":"ref7","first-page":"3003","article-title":"Quadruped robot obstacle negotiation via reinforcement learning[C]","author":"lee","year":"2006","journal-title":"Proceedings 2006 IEEE International Conference on Robotics and Automation 2006 ICRA 2006"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.639"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022140919877"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3182\/20020721-6-ES-1901.00469"},{"key":"ref21","article-title":"A modified swarm optimizer","author":"shi","year":"1998","journal-title":"IEEE International Conference of Evolutionary Computation"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-3285-9_6"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/EJP.v8-125"},{"key":"ref26","first-page":"2863","article-title":"Action-conditional video prediction using deep networks in atari games[C]","author":"oh","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.5858\/arpa.2016-0471-ED"}],"event":{"name":"2020 IEEE 16th International Conference on Control & Automation (ICCA)","start":{"date-parts":[[2020,10,9]]},"location":"Singapore","end":{"date-parts":[[2020,10,11]]}},"container-title":["2020 IEEE 16th International Conference on Control & Automation (ICCA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9264304\/9264308\/09264548.pdf?arnumber=9264548","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:19:10Z","timestamp":1656375550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9264548\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,9]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/icca51439.2020.9264548","relation":{},"subject":[],"published":{"date-parts":[[2020,10,9]]}}}