DEEP REINFORCEMENT LEARNING Yuxi Li (
[email protected]) ABSTRACT We discuss deep reinforcement learning in an overview style. We draw a big pic- ture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical con- texts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi- agent RL, relational RL, and learning to learn. After that, we discuss RL appli- cations, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transporta- tion, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue. 1 Keywords: deep reinforcement learning, deep RL, algorithm, architecture, ap- plication, artificial intelligence, machine learning, deep learning, reinforcement learning, value function, policy, reward, model, exploration vs. exploitation, representation, attention, memory, unsupervised learning, hierarchical RL, multi- agent RL, relational RL, learning to learn, games, robotics, computer vision, nat- ural language processing, finance, business management, healthcare, education, energy, transportation, computer systems, science, engineering, art arXiv:1810.06339v1 [cs.LG] 15 Oct 2018 1Work in progress. Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine Learning. This manuscript is based on our previous deep RL overview (Li, 2017). It benefits from discussions with and comments from many people. Acknowledgements will appear in a later version.