Deep reinforcement learning is one of AI’s hottest fields. Researchers, engineers, and investors are excited by its world-changing potential. In this advanced program, you’ll master techniques like Deep Q-Learning and Actor-Critic Methods, and connect with experts from NVIDIA and Unity as you build a portfolio of your own reinforcement learning projects.
Master the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.
Overview
Syllabus
- Foundations of Reinforcement Learning
- Master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.
- Value-Based Methods
- Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.
- 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.
- Multi-Agent Reinforcement Learning
- Learn how to 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.
Taught by
Alexis Cook, Arpan Chakraborty, Mat Leonard, Luis Serrano, Cezanne Camacho, Dana Sheahan, Chhavi Yadav, Juan Delgado, Miguel Morales, Bardia H., Ross A., Camilo G., Fabian V., Ho Chit S. and Robson M.
