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.
![Using Asyncio in Python [Understanding Python’s Asynchronous Programming Features]](https://www.rulit.me/data/programs/images/using-asyncio-in-python-understanding-python-s-asynchronous_606937.jpg "Python 3.4 introduced the asyncio library, and Python 3.5 produced the async and await keywords to use it palatably. These new additions allow so-called asynchronousprogramming.
All of these new features, which I’ll refer to under the single name Asyncio, have been received by the Python...")
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