Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Mixed Autonomy Traffic: A Reinforcement Learning Perspective

Explore the intersection of reinforcement learning and mixed autonomy traffic systems in this 32-minute lecture by Cathy Wu from MIT. Delve into the challenges of quantifying the impact of technology on societal systems, focusing on transportation in the US from 2020 to 2049. Examine urban simulation, single-lane dynamical systems, and human driver models. Address the critical challenge of scaling deep reinforcement learning in complex environments with combinatorial possibilities. Discover transfer learning across networks and zero-shot transfer techniques. Gain insights into counterfactual reasoning for societal systems and its potential applications in addressing the increasing pace of change and complexity in modern society.