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