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ECE 590: Reinforcement Learning at Scale

Letcture Time, Place: MW 8:30-9:45, 208 HH

Recitation Time, Place: TEER 106 10:05-11:20

Office hour: W 10-11, CIEMAS 3431

Instructor: Jay Hineman, Ph.D. (first name dot last name at institution)

TA: Zeyu Chen (first name dot last name at institution)

Short Description: This course consist of three parts. The first part will focus on machine learning at scale using modern tools such as Docker, GitLab with CI/CD, cloud computing, and Kubernetes. The second part will focus on reinforcement learning (RL) for single- and multi- agent environments and include topics such as Q-learning, policy gradients, and their deep learning extensions. The third part will combine the first two topics and focus on scaling DeepRL methods to attack large problems such as the Atari-57 benchmark and the StarCraft Multi-Agent Challenge.

Details

Evaluation/Homework/Grading

50% HW, 20% Midterm, 30% Projects (including a final project)

Resources

Books:

  • Reinforcement Learning Sutton and Barto 2018
  • Reinforcement Learning and Optimal Control Bertsekas

Projects:

  • Open AI Spinning Up
  • Kubeflow
  • Ray / Rllib
  • Horizon
  • Open AI baselines (and stable baselines fork**
  • Chainer RL

Internal resources

Proposed Content (in an ideal world)

Topic Description Lectures Assignment(s)
Docker Dockerize spinningup content 1 HW 1
MDPs and variations Define basic problem in RL and variations 2
Taxonomy of approaches Define basic solution methods 2
Review of Neural Networks Review use of NN in RL 3
Genral policy Optimization Mathematical details on gradient policy optimization techniques 3, 4
Practical policy Optimaization Explore practical algorithms and variations in spinning up 5, 6 HW 2
End of January
Group presentations Groups present from papers 7 or recitation
Ray, Rllib Production tools for RL at scale 8
Kubernetes Orchestrate docker containers using Kubernetes 9 HW3
Practicum on methods so far Comparing methods and implementations on OpenAI gym 10, 11
Q learning Introduction to Q-learning 11, 12
End of February
Q learning + PG explorre connections between Q learning and PG 13 HW4
Group presentation Groups present from papers 14 or recitation
Multiagent RL (MARL) Introduction and challenges 15
Multiagent RL methods MARL methods plain and fancy 16, 17
Practicum on MARL Demonstrate production MARL methods 18 HW 5
End of March
Capstone: Starcraft II, SMAC Introduce Starcraft challenge and multiagent version 19
bonus hyperparameter tuning Automatic tuning methods and ray.tune 20
bonus evolutionary methods Evolutionary techniques 21
Final individual presentations 1 22
Final individual presentations 2 23
Buffer 24-28

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