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Reinforcement Learning

Books
Reinforcement Learning Basic Tutorials
Online Courses
Lecture 3: TensorFlow and Neural Nets Review Session (notebook)
Lecture 23: Guest Lecture: Craig Boutilier
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)
Markov Decision Process
Value Iteration / Policy Iteration / Q-Learning
Actor-Critic Methods
Policy Gradient Methods
Sampling - Tree search
Optimization
Model-based learning
Deep Reinforcement Learning
Deep Q-network (DQN)
OpenAI Tutorials