CS 294112 at UC Berkeley
Deep Reinforcement Learning
Lectures: Wed/Fri 1011:30 a.m., Soda Hall, Room 306
The lectures will be streamed and recorded. The course is not being offered as an online course, and the videos are provided only for your personal informational and entertainment purposes. They are not part of any course requirement or degreebearing university program.
Enrollment is now closed. If you are not officially enrolled through CalCentral, please direct your questions to the subreddit.
Materials from previous offerings of this course are available here.






uGSI Soroush Nasiriany
snasiriany@berkeley.edu
Week 1 Overview
Course Introduction, Imitation Learning
Week 3 Overview
Policy Gradients and Actor Critic
Week 4 Overview
QLearning
Week 5 Overview
Advanced Policy Gradients and Control
Week 6 Overview
Model Based Reinforcement Learning
Week 7 Overview
Model Based RL and Probabilistic Models
Week 8 Overview
Inference and Control
 Homework 3: QLearning and ActorCritic
 Homework 4: ModelBased RL
 Lecture 15: Connection between Inference and Control
 Lecture 16: Inverse Reinforcement Learning
Homeworks
See Syllabus for more information.
Lecture Slides
See Syllabus for more information.
 Lecture 1: Introduction and Course Overview
 Lecture 2: Supervised Learning and Imitation
 Lecture 3: TensorFlow and Neural Nets Review Session (notebook)
 Lecture 4: Reinforcement Learning Introduction
 Lecture 5: Policy Gradients Introduction
 Lecture 6: ActorCritic Introduction
 Lecture 7: Value Functions and QLearning
 Lecture 8: Advanced QLearning Algorithms
 Lecture 9: Advanced Policy Gradients
 Lecture 10: Optimal Control and Planning
 Lecture 11: ModelBased Reinforcement Learning
 Lecture 12: Advanced Model Learning and Images
 Lecture 13: Learning Policies by Imitating Other Policies
 Lecture 14: Probability and Variational Inference Primer
 Lecture 15: Connection between Inference and Control
 Lecture 16: Inverse Reinforcement Learning
 Lecture 17: Exploration: Part 1
 Lecture 18: Exploration: Part 2
 Lecture 19: Transfer Learning and MultiTask Learning
 Lecture 20: MetaLearning
 Lecture 21: Parallelism and RL System Design
 Lecture 22: Advanced Imitation Learning and Open Problems
 Lecture 23: Guest Lecture: Craig Boutilier
 Lecture 24: Guest Lecture: Kate Rakelly & Gregory Kahn
 Lecture 25: Guest Lecture: Quoc Le
 Lecture 26: Guest Lecture: Karol Hausman
 Lecture 27: Final Project Presentations: Part 1
 Lecture 28: Final Project Presentations: Part 2