Flow Tutorials and Workshops
Tutorials and Workshops for getting started with deep reinforcement learning (RL), Flow project, and transportation.
Flow Tutorials on Python Jupyter Notebooks
| Tutorial | Title | Jupyter Notebook |
|---|---|---|
| Initalization | Setup for the tutorials | Read Setup |
| Tutorial 0 | High-level Introduction to Flow | Open Notebook |
| Tutorial 1 | Running Sumo Simulations in Flow | Open Notebook |
| Tutorial 2 | Running Aimsun simulations in Flow | Open Notebook |
| Tutorial 3 | Running RLlib Experiments | Open Notebook |
| Tutorial 4 | Visualizing Experiment Results | Open Notebook |
| Tutorial 5 | Creating Custom Networks | Open Notebook |
| Tutorial 6 | Networks from OpenStreetMap | Open Notebook |
| Tutorial 7 | Networks from Custom Templates | Open Notebook |
| Tutorial 8 | Creating Custom Environments | Open Notebook |
| Tutorial 9 | Custom Controllers | Open Notebook |
| Tutorial 10 | Traffic Lights | Open Notebook |
| Tutorial 11 | Inflows | Open Notebook |
| Tutorial 12 | Bottlenecks Experiments | Open Notebook |
| Tutorial 13 | Running rllab experiments on EC2 | Open Notebook |
ITSC 2018 Tutorial on Deep Reinforcement Learning and Transportation
Sunday, November 4th, 2018 | Maui, Hawaii
| Session | Title | Slides | Description |
|---|---|---|---|
| 1 | Welcome, opening remarks | Download | Why deep RL and transportation? |
| 2 | Reinforcement learning and approximate dynamic programming |
|
Foundations of RL |
| 3 | Policy optimization methods (policy gradient methods and non-policy gradient methods) |
|
Foundations of RL |
| 4 | Deep RL from a transportation lens (model-based RL and inverse RL) | Download | Prior research at the intersection of RL and transportation |
| 5 | Tools of the trade (SUMO, Flow, Ray RLlib) |
|
Software and simulation tools for conducting research in deep RL and transportation |
| 6 | Hands-on tutorial on //Flow | Download | Hands-on exercises with //Flow for getting started with empirical deep RL and transportation |
| 7 | Advanced topics in deep reinforcement learning (multi-agent RL, representation learning) | Download | Compelling topics for further exploration in deep RL and transportation |
CDC 2019 Workshop on Lagrangian Control for Traffic Flow Smoothing in Mixed Autonomy Settings
Sunday, December 10th, 2019 | Nice, France. Website
| Session | Title | Slides | Description |
|---|---|---|---|
| 1 | Means Field Models | Download | Use of mean field equations to model the aggregate state of a traffic system actuated |
| 2 | Deep Reinforcement Learning (RL) | Download | Techniques for applying scalable RL techniques to mixed-autonomy traffic |
| 3 | Verification of Deep Neural Networks (DNNs) | Download | techniques for verifying the safety properties of DNNs using algorithms for satisfiability modulo convex optimization. |
Please check this page again! New tutorials will be added in the near future.