DeepReinforcementLearning

CS 294: Deep Reinforcement Learning, Spring 2017: 【Berkeley CS 294：深度增强学习，2017 年春季学期】学习资源(附字幕)

Deep Reinforcement Learning 进阶 Roadmap(一)RL base & DQN-DDPG-A3C introduction

Deep Reinforcement Learning: An Overview: 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 a collection of RL resources, we close with discussions.

NanoNets: NanoNets : How to use Deep Learning when you have Limited Data

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