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