Controllers

In addition to training Deep Reinforcement Learning policies, Gym-Khana includes several example classical controllers for path tracking and vehicle stabilization.

Pure Pursuit

A Pure Pursuit waypoint follower that tracks the raceline using a lookahead circle intersection method.

Source: examples/waypoint_follow.py

cd examples
python3 waypoint_follow.py

The PurePursuitPlanner class finds the nearest point on the raceline trajectory, projects a lookahead circle forward, and computes the steering angle from the Ackermann geometry. It also includes render callbacks for visualizing the lookahead point and local plan.

Stanley and PD Controllers

Source: examples/controllers/steer_controller.py

Three steering controllers are provided, all operating on Frenet-frame observations:

PDSteerController

Path tracking controller that minimizes lateral deviation (frenet_n) and heading error (frenet_u) with proportional gains.

PDStabilityController

Recovery controller that directly minimizes sideslip angle (beta) and yaw rate (r) to stabilize an out-of-control vehicle. Useful as a baseline for comparing against learned recovery policies.

StanleyController

Stanley path tracking controller with speed-adaptive cross-track error correction. Combines heading error correction with a nonlinear cross-track term that provides aggressive low-speed corrections and gentle high-speed corrections:

delta = k_heading * theta_e + arctan(k * e / (|v| + k_soft))

MPC Controllers

Model Predictive Control implementations.

Source: examples/controllers/mpc/

Note

MPC controllers require acados built from source. See Additional dependencies in the Installation guide for setup instructions.

Kinematic MPC (examples/controllers/mpc/kmpc/)

Uses the kinematic single-track model for trajectory optimization. Suitable for low-speed scenarios.

Example usage:

cd examples
python3 kmpc_race_example.py

Single-Track MPC (examples/controllers/mpc/stmpc/)

Uses the single-track dynamic model for trajectory optimization. Handles higher speeds and tire slip dynamics.

Example usage:

cd examples
python3 stmpc_race_example.py

Both MPC controllers use a GymBridge interface (examples/controllers/mpc/gym_bridge.py) that translates between the Gym observation/action format and the MPC solver’s state/input representation.