Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.

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Course Description

Deep Reinforcement Learning
Learn cutting-edge deep reinforcement learning algorithms "from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.
Write your own implementations of many cutting-edge algorithms, including DQN, DDPG, and evolutionary methods.

Instructor Details

Alexis Cook

Alexis is an applied mathematician with a Masters in Computer Science from Brown University and a Masters in Applied Mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.