Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this bo...(展开全部)

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while inte...(展开全部)

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while inte...(展开全部)

沃尔特·皮特金，曾任美国哥伦比亚大学新闻学教授，并担任过大英百科全书美国分册的主编。他14岁开始便靠给人送货来谋生，16～18岁靠帮助政府部门在底特律的波兰人居住区进行在校学生状况调查，为自己赚得了跨人大学校门所需的第一笔学费，后一直靠勤工俭学坚持自己的学业。他广泛涉猎各种知识，学习了希腊语、拉丁语、法语、德语、希伯莱语、阿拉伯语等多种语言，一直从事哲学与心理学领域的研究。他一生完成了多部著作，涉及个人成长学习、生活与心理等多个方面。《人生40才开始》这本书当年出版后即被评为《纽约时报》年度畅销书，改变了很多美国人的生活态度与人生观。

沃尔特·皮特金，曾任美国哥伦比亚大学新闻学教授，并担任过大英百科全书美国分册的主编。他14岁开始便靠给人送货来谋生，16～18岁靠帮助政府部门在底特律的波兰人居住区进行在校学生状况调查，为自己赚得了跨人大学校门所需的第一笔学费，后一直靠勤工俭学坚持自己的学业。他广泛涉猎各种知识，学习了希腊语、拉丁语、法语、德语、希伯莱语、阿拉伯语等多种语言，一直从事哲学与心理学领域的研究。他一生完成了多部著作，涉及个人成长学习、生活与心理等多个方面。《人生40才开始》这本书当年出版后即被评为《纽约时报》年度畅销书，改变了很多美国人的生活态度与人生观。

沃尔特·皮特金，曾任美国哥伦比亚大学新闻学教授，并担任过大英百科全书美国分册的主编。他14岁开始便靠给人送货来谋生，16～18岁靠帮助政府部门在底特律的波兰人居住区进行在校学生状况调查，为自己赚得了跨人大学校门所需的第一笔学费，后一直靠勤工俭学坚持自己的学业。他广泛涉猎各种知识，学习了希腊语、拉丁语、法语、德语、希伯莱语、阿拉伯语等多种语言，一直从事哲学与心理学领域的研究。他一生完成了多部著作，涉及个人成长学习、生活与心理等多个方面。《人生40才开始》这本书当年出版后即被评为《纽约时报》年度畅销书，改变了很多美国人的生活态度与人生观。

沃尔特·皮特金，曾任美国哥伦比亚大学新闻学教授，并担任过大英百科全书美国分册的主编。他14岁开始便靠给人送货来谋生，16～18岁靠帮助政府部门在底特律的波兰人居住区进行在校学生状况调查，为自己赚得了跨人大学校门所需的第一笔学费，后一直靠勤工俭学坚持自己的学业。他广泛涉猎各种知识，学习了希腊语、拉丁语、法语、德语、希伯莱语、阿拉伯语等多种语言，一直从事哲学与心理学领域的研究。他一生完成了多部著作，涉及个人成长学习、生活与心理等多个方面。《人生40才开始》这本书当年出版后即被评为《纽约时报》年度畅销书，改变了很多美国人的生活态度与人生观。

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.This Learning Path includes content from the following Packt products:Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran.Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani.

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.This Learning Path includes content from the following Packt products:Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran.Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algor...(展开全部)