CS885 Lecture 1a: Course Introduction

(52:22)

CS885 Lecture 3b: Introduction to RL

(1:24:12)

CS885 Module 1: Trust region \u0026 proximal policy optimization

(22:18)

CS885 Lecture 14c: Trust Region Methods

(20:19)

CS885 Lecture 8a: Multi-armed bandits

(57:15)

CS885 Lecture 16a: The Option-Critic Architecture (Presenter: Zebin Kang)

(14:53)

CS885 Lecture 4a: Deep Neural Networks

(47:7)

CS885 Lecture 17a: Target-Driven Visual Navigation (Presenter: James Cagalawan)

(24:35)

CS885 Lecture 14a: Mastering the Game of Go (Presenter: Henry Chen)

(26:35)

CS885 Lecture 6a: OpenAI Environments (Mike Rudd)

(23:13)

CS885 Lecture 19c: Memory Augmented Networks

(47:27)

CS885 Module 5: Distributional RL

(23:28)

CS885 Lecture 15b: Proximal Policy Optimization (Presenter: Ruifan Yu)

(18:14)

CS885 Lecture 10: Bayesian RL

(1:22:28)

CS885 Module 6: Inverse RL

(37:46)

CS885 Lecture 20a: Neural map: structured memory for deep RL (Presenter: Andreas Stöckel)

(22:36)

CS885 Lecture 18b: Learning Driving Styles for Autonomous Vehicles (Presenter: Marko Ilievski)

(18:8)

CS885 Lecture 18a: Safe multi-agent RL for autonomous driving (Presenter: Ashish Gaurav)

(27:40)