A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions

2026-05-18

AI Summary

This work addresses the challenge of simultaneously achieving high trajectory-tracking accuracy, stability, and tunable response speed in quadrotor reinforcement learning control. The authors propose a heuristic, adjustable control method based on the Proximal Policy Optimization (PPO) algorithm, incorporating a reward function with dual-bandwidth exponential terms and an episode truncation mechanism. Within six million environment steps, the method efficiently trains a policy exhibiting critically damped responses and approximately 2% steady-state error. By adjusting reward weights and exponential coefficients, the controller can flexibly switch between acrobatic-like and inspection-like operational modes while maintaining stable performance. Experimental results across 100 random initial conditions demonstrate that the proposed approach achieves precise, tunable, and sample-efficient control in both position and yaw tracking.

Abstract

Reinforcement learning (RL)-based quadrotor control policies have achieved impressive performance in tasks such as fast navigation in cluttered environments and drone racing, where the focus is on speed and agility. However, in several applications, such as infrastructure inspection, it is critical to achieve precise, controlled maneuvers with tunable performance. In this article, we present a novel heuristic approach to achieve tunable performance in RL-based Quadrotor control through reward design and termination conditions. We present a novel reward structure containing dual bandwidth exponentials that achieves a baseline critically damped response in setpoint tracking, with low steady-state errors. When trained with a Proximal Policy Optimization (PPO) algorithm, in conjunction with episode truncation conditions, the desired performance is achieved in 6 million time steps in a sample-efficient manner. In order to tune the performance about the baseline behavior, we present intuitive heuristic rules to adjust the reward weights and exponential coefficients to achieve faster (acrobatic-like) and slower (inspection-like) settling time performance, while retaining the baseline critically damped response and approximately 2\% steady-state error. We evaluate the three RL policies (baseline, acrobatic, and inspection) across 100 trials and show accurate and tunable performance in position and yaw tracking from random initial conditions, thereby demonstrating the effectiveness of the proposed heuristic approach.

Problem

• quadrotor control
• reinforcement learning
• performance tuning
• reward design
• setpoint tracking

Innovation

• reward design
• termination conditions
• tunable performance
• quadrotor control
• reinforcement learning