Accordingly,thestatecorresponds to the selected data for labelling and their labels, and each step in the active learning algorithm corresponding to a selctionaction, wherein the heuristic selects the next items from a pool. 当预算消耗完毕的时候,该过程终止。 2.2 Steam-based learning: 之所以会有这...
最终目的是在三个回合内获得最高分. 2013年12月,总部在伦敦的 Deepmind 公司的团队发表论文:Playing Atari with Deep Reinforcement Learning ("使用深度增强学习玩Atari 电脑游戏"), 详细地解释了他们使用改进的神经网络算法在包括 Atari Breakout 在内的电脑游戏的成果. Deepmind 算法设计时,把电脑游戏的最新的四帧...
[5] Lowet, Adam S., et al. "Distributional reinforcement learning in the brain."Trends in Neurosciences43.12 (2020): 980-997. 这篇文献通过区别 slow learning 和 fast learning, 提出meta learning system 可以作为前额叶的计...
[5] Lowet, Adam S., et al. "Distributional reinforcement learning in the brain." Trends in Neurosciences 43.12 (2020): 980-997. 这篇文献通过区别 slow learning 和 fast learning, 提出meta learning system 可以作为前额叶的计算机理的研究框架 - 对解决 learning to learn 等泛化问题,提供了新的解决...
Learning how to Active Learn: A Deep Reinforcement Learning Approach 2018-03-11 12:56:04 Paper:https://www.aclweb.org/anthology/D17-1063 Code:https://github.com/mengf1/PAL 1. Introduction: 对于大部分 NLP 的任务,得到足够的标注文本来进行模型的训练是一个关键的瓶颈。所以,active learning 被引...
对于DRL,往往应用于游戏领域,在机器人领域的应用往往停留于仿真,对于DRL和ROBOTICS交叉的领域是非常大的限制。看到这篇2021年的论文《How to train your robot with deep reinforcement learning: lessons we have learned》,记录一下 Abstract 现有的深度强化学习方法大多应用于视频游戏和仿真控制中,用于真实世界中的机...
"How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies". arXiv preprint arXiv:1512.02011.Francois-Lavet, Vincent, Fonteneau, Raphael, and Ernst, Damien. How to discount deep reinforcement learn- ing: Towards new dynamic strategies. arXiv preprint arXiv:1512.02011, 2015....
吴恩达《Transformer大语言模型工作原理|How Transformer LLMs Work》(deepseek-R1翻译中英字幕共计13条视频,包括:1.intro.zh_en、2.understanding language models(Word2Vec embeddings).zh_en、3.understanding language models( word embeddings).zh_en等,UP主更多精
[5] Lowet, Adam S., et al. "Distributional reinforcement learning in the brain."Trends in Neurosciences43.12 (2020): 980-997. 这篇文献通过区别 slow learning 和 fast learning, 提出meta learning system 可以作为前额叶的计算机理的研究框架 - 对解决 learning to learn 等泛化问题,提供了新的解决思...
To address these challenges, we propose a deep reinforcement learning method to learn the self-training strategy automatically. Based on neural network representation of sentences, our model automatically learns an optimal policy for instance selection. Experimental results show that our approach outperform...