Tuesdays / Thursdays, 11:30-12:50pm, Zoom! 

Instructor:
Byron Boots
email: bbootscs.washington.edu
office: Bill and Melinda Gates Center (CSE2) 210
office hours: Tuesdays 1:00pm-2:00pm

TAs:

• Sandesh Adhikary, Office Hours: Mondays 9:00am-10:00am, Fridays 11:00am-12:00pm
• Jake Sacks, Office Hours: Wednesdays 10:00am-11:00am, Thursdays 10:00am-11:00am  

TA Office Hours:

Contact: cse599U-staff@cs.washington.edu Please communicate to the instructor and TAs only through this email (unless there is a reason for privacy).

Announcements and links to Zoom Lectures will be posted via Canvas. Discussions and questions related to the material and assignemnts will take place on Piazza.

A growing number of state-of-the-art systems including field robots, acrobatic aerial vehicles, walking robots, and computer programs for games (Chess, Hex, Go, StarCraft) rely on machine learning to make decisions. The machine learning problems in these domains represent a fundamental departure from traditional classification and regression problems. The learner must contend with: a) the effect of their own actions on the world; b) sequential decision making and credit assignment; and c) the tradeoffs between exploration and exploitation. In the past ten years, the understanding of these problems have developed dramatically. One key to the advance of learning methods has been a tight integration with optimization techniques, and we will focus on this throughout the course.

This course is directed to graduate students who want to build adaptive software that interacts with the world. Although much of the material will be driven by robotics applications, anyone interested in applying learning to decision-making or an interest in complex adaptive systems is welcome.

Suggested readings will be posted in the schedule below.

As an advanced course, familiarity with basic ideas from probability, machine learning, and decision making/control will all be helpful. As the course will be project driven, prototyping skills including C, C++, and Python will also be important. Creative thought and enthusiasm are required.

Date	Due	Topic	Reading Material	HW
10/01/20		Course Overview	Course Conduct	Sign Up for Piazza
10/06/20		MDPs, Value Iteration	Notes on Markov Decision Problems MDP Slides -- Dan Klein	HW1
10/08/20		Value Iteration (continued)	Notes on Markov Decision Problems How to Design Good Tetris Players Probabilistic Robotics, Chapter 14	Think about projects!!
10/13/20		Q-Functions, Policy Iteration	Notes on Policy Iteration Policy Iteration Slides -- Dan Klein
10/15/20		The Linear Quadratic Regulator	Notes on Linear Quadratic Regulators LQR Slides -- Pieter Abbeel RL for Helicopter Flight
10/20/20	HW1	Time Varying Systems, Affine Quadratic Regulation, Tracking with LQR	Notes on Linear Quadratic Regulators Sequential Compositions of Behaviors Speeding Up Dynamic Programming LQR Trees	HW2
10/22/20		Iterative LQR, Receding Horizon Control / Model Predictive Control	Notes on Linear Quadratic Regulators Receding Horizon DDP Differentiable MPC in Pytorch An Online Learning Approach to Model Predictive Contol
10/27/20		Inverse Optimal Control	Notes on Imitation Learning Maximum Entopy IOC
10/29/20		Fitted Q-Iteration	Notes on Approximate Dynamic Programming Learning to Drive a Real Car Generalization in RL Stable Function Approximation
11/03/20	HW2	Approximate Policy Iteration	Notes on Approximate Dynamic Programming API Survey	HW3
11/05/20		TD Learning, Eligibility Traces	Notes on TD, Q-Learning Sutton & Barto: Ch. 6
11/10/20	Project Proposal	SARSA, Q-Learning, Replay Buffers	Notes on TD, Q-Learning Deep Q-Learning
11/12/20		Brute Force Simulation-Based Policy Search: Cross Entropy, Nelder Mead	Notes on Black Box Optimization Nelder Mead -- Wikipedia PEGASUS CEM Optimization Stories
11/17/20		Backpropagation	Notes on Backpropagation Deep Learning: Ch. 6 Blog Post on the Adjoint Method Fluid Control
11/19/20		Policy Gradients, Actor Critic	Notes on Policy Gradients Sutton & Barto: Ch. 13 REINFORCE Policy Gradient Methods -- Sutton et al. Policy Gradient Slides -- Levine
11/24/20	HW3	Natural Policy Gradient	Notes on Policy Gradients Natural Policy Gradient Covariant Policy Search Natural Actor Critic Trust Region Policy Optimization Actor Critic Slides -- Levine
11/26/20		No Class (Thanksgiving)
12/01/20		Online Learning, Imitation Learning, DAgger, AggreVaTeD	Notes on Imitation Learning DAgger AggreVateD
12/03/20		Iterative Learning Control	Notes on Iterative Learning Control Using Inaccurate Models in RL DAgger for SysID
12/08/20		Student Project Presentations
12/10/20		Student Project Presentations
12/16/20	Project Report	Due at 11:59pm

Final grades will be based on course projects (40%) and homework assignments (60%).

Typsetting your homework solutions in LaTex is required.

Late homework policy: Assignments are due at the beginning of class on the day that they are due. You will be allowed 3 total late days without penalty for the entire quarter. Please use these wisely, and plan ahead for conferences, travel, deadlines, unanticipated emergencies, etc. Once those days are used, you will be penalized according to the following policy:

• Homework is worth full credit at the beginning of class on the due date.
• It is worth half credit for the next 48 hours.
• It is worth zero credit after that.

Collaboration on homework: I expect that each student will conduct themself with integrity. You are researchers-in-training, and I expect that you understand proper attribution and the importance of intellectual honesty. While you are certainly encouraged to read outside sources for a deeper understanding of the course material, if you do use materials in the preration of an assignment, they must be acknowledged clearly with an appropriate citation. Unless otherwise specified, homeworks will be done individually and each student must hand in their own assignment. It is acceptable, however, for students to collaborate in figuring out answers and helping each other understand the underlying concepts. When collaborating, the "whiteboard policy" is in effect: You may discuss assignments on a whiteboard, but, at the end of a discussion the whiteboard must be erased, and you must not transcribe or take with you anything that has been written on the board during your discussion. You must be able to reproduce the results solely on your own after any such discussion. Finally, you must write the names of the students you collaborated with on each homework.

Audit policy: If you wish to audit the course, you must either:

• Do two homework assignments.
• Do the course project

Disclaimer: I reserve the right to modify any of these plans as need be during the course of the class; however, I won't do anything capriciously, anything I do change won't be too drastic, and you'll be informed as far in advance as possible.

The course project is an opportunity for you to deeply explore one (or several) of the techniques covered in class and apply them to a problem that is of interest to you. Since the projects require a substantial amount of work, you may form groups of up to three students. The research topic is up to you, as long as it makes use of adaptive control or RL methods.

Project proposals: Your proposal should be 2-3 pages, and it should introduce the problem you are trying to solve, the approach you will take, and also address the following questions:

• What are some impacts of this research?
• What is novel about the approach you are taking?
• How do learning and/or probabilistic inference techniques play a key role?
• What is your metric for success?
• What are key technical issues you will have to confront? Are there any other big challenges?
• What software or datasets will you use?
• What is your timeline? Include specific targets for the progress report.

Note on current research: You may use your current research as a course project, as long as you explore a new area of the problem, and you cannot use previous results. Your proposal should clearly state what novel part you will be tackling in your course project.

Final presentations: You’ll present your findings to the class at the end of the semester. This will be a presentation:

• No more than 5 minutes! There will be a hard cutoff.
• No more than 5 slides, exluding title slide.
• Every group member must speak.
• You must send me a copy of the slides in advance (10am on Tuesday).
• Don't "decorate" your slides with equations. If there is an equation, I expect you to explain every variable.
• Don't read your slides / show lots of text. Slides should contain brief, salient points.

Final Report: The final report will consist of one deliverable:

1. Written report: This is the detailed report of your approach and findings. You should re-state the problem you are solving and your approach, and summarize your results. The report should be no longer than a NeurIPS paper in size (8 pages including figures and tables), but a shorter and more concrete report is preferred.

Sample Projects: You should connect RL to your own research, if possible. If you don't want to do that or the connection is hard to make, here are some examples of projects that might be appropriate. These are just suggestions!

• Train autonomous cars to navigate in CARLA
• Learn how to race in Open-Ai gym with deep Q-learning
• Imitation learning for aerial vehicle control
• Train a starcraft II agent to win a minigame
• Train a reactive controller to avoid obstacles in FlightGoggles
• Consider how to safely explore in RL
• Control a MuSHR car with MPC
• Show how curriculum learning can help with difficult games

Example Environments:

Here are some enviornments that you can use for training an RL agent. You are by no means required to use any of these simulation environments. If you find other environments, feel free to share them on Piazza.

• AirSim: https://github.com/Microsoft/AirSim
• CARLA Autonomous Driving Simulator: http://carla.org/
• FlightGoggles: https://flightgoggles.mit.edu/
• OpenAI Gym: https://gym.openai.com/
• OpenAI Universe: https://universe.openai.com/
• Deepmind Lab: https://deepmind.com/blog/article/open-sourcing-deepmind-lab
• Deepmind Control: https://github.com/deepmind/dm_control
• Starcraft II Learning Environment: https://github.com/deepmind/pysc2
• Project Malmo: https://www.microsoft.com/en-us/research/project/project-malmo/
• VizDoom: http://vizdoom.cs.put.edu.pl/
• PyRobot: https://pyrobot.org/
• The Retro Learning Environment: https://github.com/nadavbh12/Retro-Learning-Environment
• UETorch: https://github.com/facebookarchive/UETorch
• MAgent: https://github.com/geek-ai/MAgent
• The Roboterrium: https://www.robotarium.gatech.edu/
• ML Agent Toolkit: https://github.com/Unity-Technologies/ml-agents
• Bullet Physics https://pybullet.org/wordpress/

I take academic integrity very seriously. Behaving with integrity is part of our responsibility to our shared learning community. Please read the UW Student Conduct Code: Academic Misconduct for more information. If you’re uncertain about if something is academic misconduct, please ask me. I am happy to discuss any questions you may have.

This course welcomes all students of all backgrounds. The computer science and computer engineering industries have significant lack of diversity. This is due to a lack of sufficient past efforts by the field toward even greater diversity, equity, and inclusion. The Allen School seeks to create a more diverse, inclusive, and equitable environment for our community and our field. You should expect and demand to be treated by your classmates and the course staff with respect. If any incident occurs that challenges this commitment to a supportive, diverse, inclusive, and equitable environment, please let the instructor know so the issue can be addressed.

University policy prohibits all forms of sexual harassment. If you feel you have been a victim of sexual harassment or if you feel you have been discriminated against, you may speak with your instructor, teaching assistant, the chair of the department, or you can file a complaint with the UW Ombudsman's Office for Sexual Harassment. Their office is located at 339 HUB, (206) 543-6028. There is a second office, the University Complaint Investigation and Resolution Office (UCIRO), who also investigate complaints. The UCIRO is located at 22 Gerberding Hall. Please see additional resources at:
http://www.washington.edu/about/ombudsman/role.html and http://f2.washington.edu/treasury/riskmgmt/UCIRO .

Accessibility & Accommodations Acknowledgements