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.
Master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.
Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.
Learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations.
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.
Leverage neural networks to train an agent to navigate a virtual world and collect as many yellow bananas as possible while avoiding blue bananas.
Train a robotic arm to reach target locations. For an extra challenge, train a four-legged virtual creature to walk!
3.Collaboration and Competition
Train a pair of agents to play tennis. For an extra challenge, train a team of agents to play soccer!