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April 10-11, 2018: Training
April 11-13, 2018: Tutorials & Conference
Beijing, CN

Deep reinforcement learning tutorial

This will be presented in English.

Arthur Juliani (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)

Attendees should have 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)

They will need a GitHub account, and a laptop with Python and TensorFlow installed.

您将学到什么 (What you'll learn)

Attendees will learn about 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)


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 offers 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 introduces a variety of RL algorithms, including Q-Learning, Policy Gradient, and Actor-Critic, and shows 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.