- Core Lecture 1 Intro to MDPs and Exact Solution Methods -- Pieter Abbeel
- Core Lecture 2 Sample-based Approximations and Fitted Learning -- Rocky Duan
- Core Lecture 3 DQN + Variants -- Vlad Mnih
- Core Lecture 4a Policy Gradients and Actor Critic -- Pieter Abbeel
- Core Lecture 4b Pong from Pixels -- Andrej Karpathy
- Core Lecture 5 Natural Policy Gradients, TRPO, and PPO -- John Schulman
- Core Lecture 6 Nuts and Bolts of Deep RL Experimentation -- John Schulman
- Core Lecture 7 SVG, DDPG, and Stochastic Computation Graphs -- John Schulman
- Core Lecture 8 Derivative-free Methods -- Peter Chen
- Core Lecture 9 Model-based RL -- Chelsea Finn
- Core Lecture 10a Utilities -- Pieter Abbeel
- Core Lecture 10b Inverse RL -- Chelsea Finn
- Frontiers Lecture I: Recent Advances, Frontiers and Future of Deep RL -- Vlad Mnih
- Frontiers Lecture II: Recent Advances, Frontiers and Future of Deep RL -- Sergey Levine