Deep Reinforcement Learning
Learn how to teach machines to solve complex games with Deep Reinforcement Learning
This title is part of the Deep Learning Mini-Degree
Reinforcement Learning allows machines and software agents to automatically determine the best course of behavior within a set context – with applications ranging from allowing computers to solve games, to autopilot systems and robot tasks training, this area of learning has never been more relevant. However, Reinforcement Learning isn’t perfect, and often has issues in dealing with more complex tasks. This is where Deep Reinforcement Learning comes in!
Through this course you will be given the theoretical understanding of Deep Reinforcement Learning as you build your own Deep Reinforcement Agents and teach them how to play complex, Atari-style games.
What you will learn:
- The theoretical concepts underlying Reinforcement Learning
- How to define games so that you can build algorithms to help solve them
- Addressing the problems inherent in Reinforcement Learning with Value Iterations
- Using Q-Learning to improve upon Value Iteration
- Using Deep Q-Networks to solve complex games
About the Deep Learning Mini-Degree
The Deep Learning Mini-Degree is an on-demand learning curriculum composed of 6 professional-grade courses geared towards teaching you how to solve real-world problems and build innovative projects using Machine Learning and Python. Learn and understand the fundamentals necessary to build the next generation of intelligent applications and software, with concepts and theory that can be applied across technology and frameworks.
Challenge yourself by joining this exciting project-based curriculum and gain the knowledge and abilities required to succeed in this brand new industry. No prior experience with AI or Machine Learning is necessary to join. However, basic to intermediate Python skills are assumed in all of the courses.
What Our Members Are Saying
- Intermediate Python programming skills
- Familiarity with Artificial Neural Networks
- Familiarity with Convolutional Neural Networks