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April 10-11, 2018: Training
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Deep reinforcement learning tutorial

This will be presented in English.

Arthur Juliani (Unity Technologies), Leon Chen (Unity Technologies)
13:3017:00 Wednesday, April 11, 2018
Secondary topics:  增强学习(Reinforcement Learning)

必要预备知识 (Prerequisite Knowledge)

应该具备机器学习方法的基本知识,包括神经网络。
A basic knowledge of machine learning methods, including neural networks

该辅导课要求硬件和/或安装 (Hardware and/or installation requirements)

一个GitHub账号,安装Python及TensorFlow的电脑。
A laptop with Python and TensorFlow installed and a GitHub account

您将学到什么 (What you'll learn)

将会学到增强学习理论的基础知识,理解如何针对解决复杂问题构建系统。
Learn the fundamentals of reinforcement learning theory and understand how it can be built upon to solve more complex problems with rewards in large state spaces

描述 (Description)

本教程将以英语授课。不会有同声传译,但您的问题会有翻译。

In the past few years, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning (RL). Unlike traditional supervised learning methods, in which networks are trained using hand-labeled data, the reinforcement learning paradigm utilizes a reward signal provided by the environment itself to train the network.

Arthur Juliani and Leon Chen lead a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks. Along the way, Arthur and Yuan introduce a variety of RL algorithms, including Q-Learning, Policy Gradient, and Actor-Critic, and show how to extend them using deep neural networks to solve problems with much more complex and varied state and action spaces.

在过去的几年间,计算机已经学会了玩Atari的游戏、下围棋、玩第一人称视角的射击游戏,而且水平已经超越了人类。所有这些成就的背后都是基于深度增强学习(Reinforcement Learning,RL)。传统的监督学习方法会使用人工标注的数据来训练神经网络。和这个方法不同,增强学习的范式使用环境自身提供的奖励信号来训练神经网络。

Arthur Juliani将会就增强学习做深入的探讨。从最基本的查找表和GridWall到使用深度神经网络解决复杂的三维任务。在这一过程中,Arthur将介绍增强学习算法的多种变形,包括Q-学习、策略梯度和Actor-Critic等。并将介绍如何扩展这些算法来使用深度神经网络来解决更加复杂、状态和行动空间可变的问题。

Photo of Arthur Juliani

Arthur Juliani

Unity Technologies

Arthur Juliani is a machine learning engineer at Unity Technologies. A researcher working at the intersection of cognitive neuroscience and deep learning, Arthur is currently working toward a PhD at the University of Oregon.

Photo of Leon Chen

Leon Chen

Unity Technologies

Leon Chen is the product marketing manager for Unity Analytics and Machine Learning, where he is responsible for driving productization and the go-to-market strategy for Unity ML-Agents, a Unity toolkit that allows developers and researchers to create and implement new AI algorithms. Previously, Leon spent over nine years as a tech evangelist, solution manager, and business manager for companies including Oracle and Microsoft. Leon holds an MBA from the University of Texas at Austin.

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Comments

Picture of Leon Chen
Leon Chen | PRODUCT MARKETING MANAGER, ANALYTICS AND MACHINE LEARNING
2018-04-25 19:31 CST

Please find the course materials in this link. https://share.weiyun.com/5yffHoB

Thanks!

Weikai Xie | SENIOR STAFF ENGINEER
2018-04-12 14:01 CST

Hi, I enrolled this tutorial but missed it due to some unexpected situation. I was wondering where I can download a copy of the course materials?