2、Lecture 1 - Motivation + Overview + Exact Solution Methods

学员 1、Lecture I - Recent Advances, Frontiers and Future of Deep RL

学员 3、Lecture 2 - Sampling-based Approximations and Function Fitting

学员 4、Lecture 3 - Deep Q-Networks

学员 5、Lecture 4A - Policy Gradients

学员 6、Lecture 4B Policy Gradients Revisited

学员 7、Lecture 5 - Natural Policy Gradients, TRPO, PPO

学员 8、Lecture 6 - Nuts and Bolts of Deep RL Experimentation

学员 9、Lecture 7 SVG, DDPG, and Stochastic Computation Graphs (John Schulman)

学员 10、Lecture 8 Derivative Free Methods

学员 11、Lecture 9 Model-based Reinforcement Learning

学员 12、Lecture 10A Utlities

学员 13、Lecture 10B Inverse Reinforcement Learning

学员 14、TAs Research Overview

学员 15、Frontiers Lecture II - Recent Advances, Frontiers and Future of Deep RL