Tutorials on Inverse Reinforcement Learning 2018-07-22 21:44:39 1. Papers:  Inverse Reinforcement Learning: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.394.2178&rep=rep1&type=pdf Cooperative Inverse Reinforcement Learning: http://pape…

Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. The papers are organized based on manually-defined bookmarks. They are sorted by time to see the recent papers first. Any suggestions and pull requests…

深度强化学习的18个关键问题 from: https://zhuanlan.zhihu.com/p/32153603 85 人赞了该文章 深度强化学习的问题在哪里?未来怎么走?哪些方面可以突破? 这两天我阅读了两篇篇猛文A Brief Survey of Deep Reinforcement Learning 和 Deep Reinforcement Learning: An Overview ,作者排山倒海的引用了200多篇文献,阐述强化学习未来的方向.原文归纳出深度强化学习中的常见科学问题,…

this blog from: https://github.com/LantaoYu/MARL-Papers Paper Collection of Multi-Agent Reinforcement Learning (MARL) This is a collection of research and review papers of multi-agent reinforcement learning (MARL). The sharing principle of these refe…

Awesome Reinforcement Learning A curated list of resources dedicated to reinforcement learning. We have pages for other topics: awesome-rnn, awesome-deep-vision, awesome-random-forest Maintainers: Hyunsoo Kim, Jiwon Kim We are looking for more contri…

在机器学习中,我们经常会分类为有监督学习和无监督学习,但是尝尝会忽略一个重要的分支,强化学习.有监督学习和无监督学习非常好去区分,学习的目标,有无标签等都是区分标准.如果说监督学习的目标是预测,那么强化学习就是决策,它通过对周围的环境不断的更新状态,给出奖励或者惩罚的措施,来不断调整并给出新的策略.简单来说,就像小时候你在不该吃零食的时间偷吃了零食,你妈妈知道了会对你做出惩罚,那么下一次就不会犯同样的错误,如果遵守规则,那你妈妈兴许会给你一些奖励,最终的目标都是希望你在该吃饭的时候吃饭,该吃零食…

CVPR2018_Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning http://mmlab.ie.cuhk.edu.hk/projects/RL-Restore/ 强化学习的入门介绍:https://blog.csdn.net/aliceyangxi1987/article/details/73327378 https://www.zhihu.com/question/41775291 CNN在l…

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 2019-07-15 22:23:02 Paper: https://arxiv.org/pdf/1801.01290.pdf or Updated Version: https://arxiv.org/pdf/1812.05905.pdf Project: https://sites.google.c…

Reinforcement Learning 对于控制决策问题的解决思路:设计一个回报函数(reward function),如果learning agent(如上面的四足机器人.象棋AI程序)在决定一步后,获得了较好的结果,那么我们给agent一些回报(比如回报函数结果为正),得到较差的结果,那么回报函数为负.比如,四足机器人,如果他向前走了一步(接近目标),那么回报函数为正,后退为负.如果我们能够对每一步进行评价,得到相应的回报函数,那么就好办了,我们只需要找到一条回报值最大的路径(每步的回…

Playing FPS games with deep reinforcement learning 博文转自:https://blog.acolyer.org/2016/11/23/playing-fps-games-with-deep-reinforcement-learning/ When I wrote up 'Asynchronous methods for deep learning' last month, I made a throwaway remark that after…