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 and Leon Chen lead 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 and Yuan introduce a variety of RL algorithms, including Q-Learning, Policy Gradient, and Actor-Critic, and show how to extend them using deep neural networks to solve problems with much more complex and varied state and action spaces.
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.
Leon Chen is the product marketing manager for Unity Analytics and Machine Learning, where he is responsible for driving productization and the go-to-market strategy for Unity ML-Agents, a Unity toolkit that allows developers and researchers to create and implement new AI algorithms. Previously, Leon spent over nine years as a tech evangelist, solution manager, and business manager for companies including Oracle and Microsoft. Leon holds an MBA from the University of Texas at Austin.
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