主动学习的无监督自举以解决实体问题Unsupervised Bootstrapping of Active Learning for Entity Resolution |
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课程网址: | http://videolectures.net/eswc2020_primpeli_paper_80/ |
主讲教师: | Anna Primpeli |
开课单位: | 曼海姆大学 |
开课时间: | 2020-05-25 |
课程语种: | 英语 |
中文简介: | 在本教程中,我将讨论如何将强化学习(RL)与深度学习(DL)相结合。有几种方法可以将DL和RL结合在一起,包括基于价值,基于策略和基于模型的规划方法。这些方法中有几种存在众所周知的分歧问题,我将介绍解决这些不稳定性的简单方法。演讲将包括一个关于Atari 2600领域最近成功案例的案例研究,在该领域中,一个代理商可以直接从原始像素输入中学习直接玩许多不同的游戏。 |
课程简介: | In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning. Several of these approaches have well-known divergence issues, and I will present simple methods for addressing these instabilities. The talk will include a case study of recent successes in the Atari 2600 domain, where a single agent can learn to play many different games directly from raw pixel input. |
关 键 词: | 主动学习; 深度学习; Atari |
课程来源: | 视频讲座网 |
数据采集: | 2020-08-03:yumf |
最后编审: | 2020-08-03:yumf |
阅读次数: | 48 |