机器学习的研究领域包括有监督学习(Supervised Learning),无监督学习(Unsupervised Learning),半监督学习(Semi-supervised Learning)和强化学习(Reinforcement Learning)等诸多内容。针对有监督学习和半监督学习,都需要一定数量的标注数据,也就是说在训练模型的时候,全部或者部分数据需要带上相应的标签才能进行模型的训练。但是...
第一种是流式的主动学习（Sequential Active Learning），第二种是离线批量的主动学习（Pool-based Active...
reinforcement learning algorithm. Let T 0 , R 0 be the user-supplied model of transition probabilities and rewards for an MDP. We can use Taylor’s approximation to model the local sensitiv- ity of U π (T 0 , R 0 ) as the transition probabilities X ∈ ∆(Next(ˆ s, ˆ a))...
active reinforcement learning:主动强化学习 下载积分: 2000 内容提示: Active Reinforcement LearningArkady Epshteyn aepshtey@google.comGoogle Inc., 4720 Forbes Ave, Pittsburgh, PA 15213 USAAdam Vogel acvogel@stanford.eduGerald DeJong mrebl@uiuc.eduComputer Science Department, University of Illinois at ...
机器学习的研究领域包括有监督学习(Supervised Learning),无监督学习(Unsupervised Learning),半监督学习(Semi-supervised Learning)和强化学习(Reinforcement Learning)等诸多内容。针对有监督学习和半监督学习,都需要一定数量的标注数据,也就是说在训练模型的时候,全部或者部分数据需要带上相应的标签才能进行模型的训练。但是...
Active learningReinforcement learningLearning from demonstrationRecent research has shown that although Reinforcement Learning (RL) can benefit from expert demonstration, it usually takes considerable ef- forts to obtain enough demonstration. The efforts prevent training decent RL agents with expert ...
Active Learning for Reward Estimation in Inverse Reinforcement Learning Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, we introduce active learning for inverse reinforcement learning. ...
2.2.5 Reinforcement Learning: 这里用的就是 DQN 算法,具体可以参考其他博客。 2.3 Cross-lingual policy transfer: 这里进行跨语言策略迁移的目的是:为了处理数据量比较少的语言中的 active learning 问题。作者采用 Transfer learning 的方法,在数据量丰富的数据集上学习一个比较好的 policy,然后将这种策略应用到 数...
基于DeepQNetwork algorithm,作者将奖励函数和增强学习(reinforcement learning setting)结合来学习一个定位策略(localization policy)。作者的结果表明,一个训练的agent可以在11步左右定位到一个物体的示例,这意味着该算法可以在处理11个区域之后准确的找到一个物体。
In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an ac...