This course is a basic introduction to reinforcement learning algorithms and their applications. Topics include: multi-armed bandits; finite Markov decision processes; dynamic programming; Monte-Carlo methods; temporal-difference learning; actor-critic methods; off-policy learning; introduction to deep variants of the aforementioned algorithms, including deep Q-learning, policy gradient methods, and actor-critic methods.
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks.
The first of the two volumes of the leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
Address:
2001 Longxiang Blvd., Longgang District,
Shenzhen, Guangdong, China
Tel: (0755)23515151 (University Library)
Email: library@cuhk.edu.cn
on WeChat
on Bilibili