Deep Reinforcement Learning

Colleagues, the Deep Reinforcement Learning training program will equip you with skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics. 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. Students should have experience with Python, probability, machine learning, and deep learning. Skill-based training modules include: 1) Foundations of Reinforcement Learning - master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods, 2) Value-Based Methods - apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data (Project: Navigation), 3) Policy-Based Methods - learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations (Project: Continuous Control), 4) Multi-Agent Reinforcement Learning - apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles (Project: Collaboration and Competition).

Enroll today (teams & execs welcome): https://tinyurl.com/4d27236r 

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