Changzhu Zhang

Trajectory-Constrained Human-Guided Reinforcement Learning for Autonomous Driving.

A new framework that produces guidance at the full-trajectory level to suit the planning horizon of autonomous driving and provides a formal criterion for deciding when and how to inject human input into the policy-update loop is proposed, leveraging the Lagrangian method to solve the constrained optimization problem.