Аннотация
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We start with background of deep learning and reinforcement learning, as well as introduction of testbeds. Next we discuss Deep Q-Network (DQN) and its extensions, asynchronous methods, policy optimization, reward, and planning.
After that, we talk about attention and memory, unsupervised learning, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, spoken dialogue systems (a.k.a. chatbot), machine translation, text sequence prediction, neural architecture design, personalized web services, healthcare, finance, and music generation. We mention topics/papers not reviewed yet. After listing acollection of RL resources,weclose withdiscussions.
![SPSS® Statistics For Dummies® [3rd Edition]](https://www.rulit.me/data/programs/images/spss0-statistics-for-dummies0-3rd-edition_567867.jpg "The ultimate beginner")
![Чистый Python [Тонкости программирования для профи]](https://www.rulit.me/data/programs/images/chistyj-python-tonkosti-programmirovaniya-dlya-profi_554913.jpg "Изучение всех возможностей Python — сложная задача, а с этой книгой вы сможете сосредоточиться на практических навыках, которые действительно важны. Раскопайте...")
Комментарии к книге "Deep reinforcement learning: an overview"