Pure Pursuit (PP) is widely used in autonomous racing for real-time path tracking due to its efficiency and geometric clarity, yet performance is highly sensitive to how key parameters-lookahead distance and steering gain-are chosen. Standard velocity-based schedules adjust these only approximately and often fail to transfer across tracks and speed profiles. We propose a reinforcement-learning (RL) approach that jointly chooses the lookahead

$L_{d}$

and a steering gain

$g$

online using Proximal Policy Optimization (PPO). The policy observes compact state features (speed and curvature taps) and outputs

$(L_{d},g)$

at each control step. Trained in F1TENTH Gym and deployed in a ROS 2 stack, the policy drives PP directly (with light smoothing) and requires no per-map retuning. Across simulation and real-car tests, the proposed RL–PP controller that jointly selects

$(L_{d},g)$

consistently outperforms fixed-lookahead PP, velocity-scheduled adaptive PP, and an RL lookahead-only variant, and it also exceeds a kinematic MPC raceline tracker under our evaluated settings in lap time, path-tracking accuracy, and steering smoothness, demonstrating that policy-guided parameter tuning can reliably improve classical geometry-based control.

Amr S. El-Wakeel

Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control With PPO