郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Abstract 在生物和人工系统的学习研究之间,已经有富有成果的概念和想法流.Bush and Mosteller,Rescorla and Wagner首先在生物中开发的学习规则启发了许多早期的工作,从而导致了针对人工系统的强化学习(RL)算法的开发.最近,为在人工智能体中学习而开发的时序差分RL为解释多巴胺神经元的活动提供了基础框架. 在本综述中,我们描述了有关生物和人工智能体中RL的最新技术.我们专注于这些学科之间的联系点,并…

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…

Reinforcement Learning for Profit July 17, 2016 Is RL being used in revenue generating systems today?   Recently, one of my facebook friends, and alumni of the University of Alberta (with a PhD in Computing Science), Cosmin Paduraru posed a question:…

Applications of Reinforcement Learning in Real World 2018-08-05 18:58:04 This blog is copied from: https://towardsdatascience.com/applications-of-reinforcement-learning-in-real-world-1a94955bcd12 There is no reasoning, no process of inference or comp…

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…

Deep Learning in a Nutshell: Reinforcement Learning   Share: Posted on September 8, 2016by Tim Dettmers No CommentsTagged Deep Learning, Deep Neural Networks, Machine Learning,Reinforcement Learning This post is Part 4 of the Deep Learning in a Nutsh…

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,…

Evolution Strategies as a Scalable Alternative to Reinforcement Learning this blog from: https://blog.openai.com/evolution-strategies/   MARCH 24, 2017 Evolution Strategies as a Scalable Alternative to Reinforcement Learning We’ve discovered that evo…

R. Amiri, M. A. Almasi, J. G. Andrews and H. Mehrpouyan, "Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks," in IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 3933-3947, Aug. 20…

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…