Deep Reinforcement Learning

Humans, animals, and robots faced with the world must make decisions and take actions in the world. Moreover, the decisions they choose affect the world they exist in - and those outcomes must be taken into account. This course is about algorithms for deep reinforcement learning - methods for learning behavior from experience, with a focus on practical algorithms that use deep neural networks to learn behavior from high-dimensional observations.

This course is complementary to CS234: Reinforcement Learning with neither being a pre-requisite for the other. In comparison to CS234, this course will have a more applied and deep learning focus and an emphasis on use-cases in robotics and motor control

Topics Include

Methods for learning from demonstrations
Both model-based and model-free deep RL methods
Methods for learning from offline datasets and more advanced techniques for learning multiple tasks such as goal-conditioned RL, meta-RL, and unsupervised skill discovery