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所在平台: Udemy |
课程主页: https://www.udemy.com/course/fundamentals-of-reinforcement-learning/
课程评论：没有评论
课程名称：强化学习基础 概述：强化学习是现代人工智能中最令人兴奋的分支之一。它的公共意识主要得益于DeepMind的早期突破：2016年，他们利用强化学习击败了被认为需要数十年才能实现的人工智能基准，战胜了世界顶级围棋高手。这一成就尤为显著，因为围棋的博弈树极为庞大，可能的走法数量高达10的200次方（即“gargoogol”）。与此相比，国际象棋的节点数量仅有10的50次方。国际象棋于1997年被IBM的深蓝击败卡斯帕罗夫，这代表了旧一代人工智能（GOFAI）的极致表现。深蓝通过人类大师手动编程的开局策略、棋子和棋盘估值及残局数据库来进行运算。然而，DeepMind的做法截然不同，他们应用强化学习，让算法通过自我博弈，反复赢输数百万次，得出有效的策略，最终形成了一种不受人类知识局限的游戏算法。 DeepMind还展示了强化学习在实际问题中的应用。一个控制谷歌数据中心冷却系统的强化学习代理找到了一些人类控制工程师未曾想到的策略，例如利用冬季温度节省取暖能耗。另一个应用于实验性核聚变反应堆的代理同样发现了超出人类的控制策略，成功应对复杂的等离子体控制问题。 强化学习不仅有助于解决一些重大的科学和工程难题，也具备众多更为直接的商业应用，比如产品和网站设计的A/B测试、推荐系统的实施，以帮助公司客户与产品匹配，以及算法交易，以最大化利润。 本课程将讲解这一激动人心的人工智能分支的基础知识。您将深入理解算法背后的理论，并获得使用Python实现这些算法的实际经验。课程将侧重于使用简单的NumPy数组进行“表格”方法，而非深度神经网络，因为通过简化问题，我们能获得更大的理解。在每个章节末尾都有编程作业，让您亲自构建算法。 到课程结束时，您将对这些算法有基本的掌握。此后，您可以去解决自动驾驶、在对冲基金中赚取巨额利润，或通过解决核聚变能源问题来拯救人类！
Reinforcement learning is one of the most exciting branches of modern artificial intelligence.It came to the public consciousness largely because of a brilliant early breakthrough of DeepMind: in 2016, they utilised reinforcement learning to smash a benchmark thought to be decades away in artificial intelligence - they beat the world's greatest human grandmaster in the Chinese game of Go.This was so exceptional because the game tree for Go is so large - the number of possible moves is 1 with 200 zeros after it (or a "gargoogol"!). Compare this with chess, which has only 10^50 nodes in its tree.Chess was solved in 1997, when IBM's Deep Blue beat the world's best Gary Kasparov. Deep Blue was the ultimate example of the previous generation of AI - Good Old-fashioned AI or "GOFAI". A team of human grandmasters hard-coded opening strategies, piece and board valuations and end-game databases into a powerful computer which then crunched the numbers in a relatively brute-force way.DeepMind's approach was very different. Instead of humans hard-coding heuristics for how to play a good game of Go, they applied reinforcement learning so that their algorithms could - by playing themselves, and winning or losing millions of times - work out good strategies for themselves.The result was a game playing algorithm unbounded by the limitations of human knowledge. Go grandmasters to this day are studying its unique and creative moves in its series against Lee Sedol.Since then, DeepMind have shown how reinforcement learning can be practically applied to real life problems. A reinforcement learning agent controlling the cooling system for a Google data centre found strategies no human control engineer had thought of, such as to exploit winter temperatures to save heater use. Another of their agents applied to an experimental fusion reactor similarly found superhuman strategies for controlling the highly complex plasma in the reactor.So, reinforcement learning promises to help solve some of the grand problems of science and engineering, but it has a whole load of more immediately commercial applications too - from the A/B testing of products and website design, to the implementation of recommender systems to learn how to match up a company's customers with its products, to algorithmic trading, where the objective is to buy or sell stocks to maximise a profit.This course will explain the fundamentals of this most exciting branch of AI. You will get to grips with both the theory underpinning the algorithms, and get hands-on practise implementing them yourself in python.By the end of this course, you will have a fundamental grasp these algorithms. We'll focus on "tabular" methods using simple NumPy arrays rather than neural networks, as one often gets the greatest understanding of problems by paring them down to their simplest form and working through each step of an algorithm with pencil and paper.There is ample opportunity for that in this course, and each section is capped with a coding assignment where you will build the algorithms yourselfFrom there, the world is your oyster! Go solve driverless cars, make bajillions in a hedge fund, or save humanity by solving fusion power!
