Become a Deep Reinforcement Learning Expert

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.

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.

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