Lectures
- Core Lecture 1 Intro to MDPs and Exact Solution Methods -- Pieter Abbeel (video | slides)
- Core Lecture 2 Sample-based Approximations and Fitted Learning -- Rocky Duan (video | slides)
- Core Lecture 3 DQN + Variants -- Vlad Mnih (video | slides)
- Core Lecture 4a Policy Gradients and Actor Critic -- Pieter Abbeel (video | slides)
- Core Lecture 4b Pong from Pixels -- Andrej Karpathy (video | slides)
- Core Lecture 5 Natural Policy Gradients, TRPO, and PPO -- John Schulman (video | slides)
- Core Lecture 6 Nuts and Bolts of Deep RL Experimentation -- John Schulman (video | slides)
- Core Lecture 7 SVG, DDPG, and Stochastic Computation Graphs -- John Schulman (video | slides)
- Core Lecture 8 Derivative-free Methods -- Peter Chen (video | slides)
- Core Lecture 9 Model-based RL -- Chelsea Finn (video | slides)
- Core Lecture 10a Utilities -- Pieter Abbeel (video | slides)
- Core Lecture 10b Inverse RL -- Chelsea Finn (video | slides)
- Frontiers Lecture I: Recent Advances, Frontiers and Future of Deep RL -- Vlad Mnih (video | slides)
- Frontiers Lecture II: Recent Advances, Frontiers and Future of Deep RL -- Sergey Levine (video | slides)
- TAs Research Overviews (video | slides)
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.
No comments:
Post a Comment