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Building deep reinforcement learning applications on BigDL and Spark

This will be presented in English.

Arsenii Mustafin (Fudan University)
14:0014:40 Friday, April 13, 2018
Secondary topics:  增强学习(Reinforcement Learning), 深度学习(Deep Learning)

必要预备知识 (Prerequisite Knowledge)

A basic understanding of BigDL and Spark

您将学到什么 (What you'll learn)

Understand how to do RL on BigDL and Spark

描述 (Description)


Deep reinforcement learning is a thriving area—DeepMind’s AlphaGo, for instance, has drawn the attention of the entire world. But besides playing games, deep reinforcement learning (DRL) also has many practical applications in industry, such as autonomous driving, chatbots, financial investment, inventory management, and even recommendation systems. Although DRL applications are similar to supervised computer vision or natural language processing tasks, they are unique in many ways. For example, they have to interact with or explore the environment to obtain training samples along the optimization, and the method to improve the model is usually different from common supervised applications.

BigDL, a well-developed deep learning library on Spark, is handy for big data users but has been mostly used for supervised and unsupervised machine learning. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark, discussing extensions particularly for DRL algorithms (DQN, PG, PPO, Actor-Critic, etc.). You’ll get tips on how to build a RL application for your own use case.

深度增强学习(Deep ReinforcementLearning,DRL)是当今AI战场里一个蓬勃发展的领域。DeepMind的AlphaGO是DRL一个非常成功的应用,获得了全世界的关注。除了能玩游戏,DRL还能在多个行业有很多应用,比如无人驾驶、对话机器人、金融投资、库存管理、甚至是推荐系统。虽然DRL的应用和监督学习型的计算机视觉或自然语言处理任务在某些方面有相同点,但它在很多方面都是非常独特的。例如,DRL的应用必须要通过和环境的交互(探索)来获得训练样本并优化,而且DRL用于改进模型的方法和通常的监督学习型应用也有所不同。


Photo of Arsenii Mustafin

Arsenii Mustafin

Fudan University

Arsenii Mustafin is a Russian PhD student at Fudan University, where he specializes in economic studies and data analysis.