Letcture Time, Place: MW 8:30-9:45, 208 HH
Recitation Time, Place: Consult dukehub ((TODO: Find this information))
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.
50% HW, 20% Midterm, 30% Projects (including a final project)
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
| 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 |