Courses, Stanford Online

Stanford CS224R Deep Reinforcement Learning

19 lectures, 20 hr 37 min in all

Humans, animals, and robots must make decisions and take actions in the world. Their choices impact the environment around them, and it is essential to consider the consequences of those decisions. This course is about algorithms for deep reinforcement learning, including methods for learning behavior from experience, with a focus on practical algorithms that use deep neural networks to learn behavior from high-dimensional observations. 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. These methods will be instantiated with examples from domains with high-dimensional state and action spaces, such as robotics, visual navigation, and control. This course is complementary to CS234, which is not a prerequisite 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 language modeling.