Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Simons Institute via YouTube Direct link

Intro

1 of 11

1 of 11

Intro

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 Counterfactual reasoning with • Motivation: Quantify impact of technology on societal systems • Pace of change & complexity is increasing
  3. 3 Years 2020 to 2049: Mixed autonomy Transportation in the US
  4. 4 Urban simulation
  5. 5 Axes of difficulty in mixed autonomy
  6. 6 Single-lane: dynamical system equil Human driver model
  7. 7 Challenge: combinatorial number of environn A critical challenge to scaling deep reinforcement learning
  8. 8 Transfer learning across networ
  9. 9 Zero-shot transfer
  10. 10 The road ahead: counterfactual reasoning for societa Motivation Quantity impact of technology on societal systems
  11. 11 Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.