Udacity – Deep Reinforcement Learning Nanodegree V1.0.0
Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.
What you’ll learn
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.
Requirements
- This program requires experience with Python, probability, machine learning, and deep learning.
Description
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.