Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Simons Institute via YouTube Direct link

Single-lane: dynamical system equil Human driver model

6 of 11

6 of 11

Single-lane: dynamical system equil Human driver model

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Mixed Autonomy Traffic: A Reinforcement Learning Perspective

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  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

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