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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. Topics will 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 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.