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 Running RLlab (previous version of RLlib) Experiments Open Notebook
Tutorial 5 Visualizing Experiment Results Open Notebook
Tutorial 6 Creating Custom Scenarios Open Notebook
Tutorial 7 Networks from OpenStreetMap Open Notebook
Tutorial 8 Networks from Custom Templates Open Notebook
Tutorial 9 Creating Custom Environments Open Notebook
Tutorial 10 Custom Controllers Open Notebook
Tutorial 11 Traffic Lights Open Notebook
Tutorial 12 Inflows 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.

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