Building Deep Reinforcement Learning Applications on Apache Spark with Analytics Zoo using BigDL

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Deep Reinforcement Learning (DRL) is a thriving area in the current AI battlefield. AlphaGO by DeepMind is a very successful application of DRL which has drawn the attention of the entire world. Besides playing games, DRL also has many practical use in industry, e.g. autonomous driving, chatbots, financial investment, inventory management, and even recommendation systems. Although DRL applications has something in common with supervised Computer Vision or Natural Language Processing tasks, they are unique in many ways.

For example, they have to interact (explore) with the environment to obtain training samples along the optimization, and the method to improve the model is usually different from common supervised applications. In this talk we will share our experience of building Deep Reinforcement Learning applications on BigDL/Spark. BigDL is a well-developed deep learning library on Spark which is handy for Big Data users, but it has been mostly used for supervised and unsupervised machine learning. We have made extensions particularly for DRL algorithms (e.g. DQN, PG, TRPO and PPO, etc.), implemented classical DRL algorithms, built applications with them and did performance tuning. We are happy to share what we have learnt during this process.

We hope our experience will help our audience learn how to build a RL application on their own for in their production business.

Session hashtag: #DLSAIS10



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About Yuhao Yang

Yuhao Yang is a software engineer at Intel, where he provides implementation, consulting, and tuning advice on the Hadoop ecosystem to industry partners. Yuhao’s area of focus is distributed machine learning, especially large-scale analytical applications and infrastructure on Spark. He’s also an active contributor to Spark MLlib (50+ patches), has delivered the implementation of online LDA, QR decomposition, and several transformers of Spark feature engineering, and has provided improvements on some important algorithms.