Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning | |
Huang, Wenzhen1,2![]() ![]() ![]() ![]() | |
2021-02 | |
会议名称 | The Thirty-Fifth AAAI Conference on Artificial Intelligence |
会议日期 | 2021-2 |
会议地点 | online |
出版者 | AAAI Press |
摘要 | Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inaccurate or biased, imaginary trajectories may be deleterious for training the action-value and policy functions. To alleviate such problem, this paper proposes to adaptively |
收录类别 | EI |
七大方向——子方向分类 | 机器学习 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46602 |
专题 | 模式识别实验室 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 2.CRISE, Institute of Automation, Chinese Academy of Sciences, Beijing, China 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Huang, Wenzhen,Yin Qiyue,Zhang Junge,et al. Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning[C]:AAAI Press,2021. |
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2136.HuangW.pdf(5676KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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