This course is all about the application of deep learning and neural networks to reinforcement learning.
If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI.
Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.
Reinforcement learning has been around since the 70s but none of this has been possible until now.
The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.
We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.
Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward.
Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus – they want to reach a goal.