{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:06:33Z","timestamp":1775912793383,"version":"3.50.1"},"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":[[2020,7]]},"abstract":"<jats:p>Transfer learning has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing approaches either transfer previous knowledge by explicitly computing similarities between tasks or select appropriate source policies to provide guided explorations. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarities is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) by taking advantage of this idea. PTF learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as an option learning problem. PTF can be easily combined with existing DRL methods and experimental results show it significantly accelerates RL and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/428","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"3094-3100","source":"Crossref","is-referenced-by-count":24,"title":["Efficient Deep Reinforcement Learning via Adaptive Policy Transfer"],"prefix":"10.24963","author":[{"given":"Tianpei","family":"Yang","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"},{"name":"Noah's Ark Lab, Huawei"}]},{"given":"Jianye","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"},{"name":"Noah's Ark Lab, Huawei"},{"name":"Tianjin Key Lab of Machine Learning"}]},{"given":"Zhaopeng","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}]},{"given":"Zongzhang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Yujing","family":"Hu","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab in Netease"}]},{"given":"Yingfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab in Netease"}]},{"given":"Changjie","family":"Fan","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab in Netease"}]},{"given":"Weixun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}]},{"given":"Wulong","family":"Liu","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei"}]},{"given":"Zhaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Washington State University"}]},{"given":"Jiajie","family":"Peng","sequence":"additional","affiliation":[{"name":"Tianjin University"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:15:05Z","timestamp":1594260905000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/428"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/428","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}