Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning
This work addresses the need for automatic controller tuning in domains like autonomous driving, offering a more versatile and efficient solution compared to existing methods, though it is incremental in improving upon derivative-free approaches.
The paper tackles the problem of automatically tuning controller parameters, which is tedious and often limited by non-differentiable structures, by proposing a derivative-free reinforcement learning framework. Experimental results on autonomous driving examples show it outperforms popular baselines, demonstrating strong potential for efficient tuning.
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods. However, among existing methods, derivative-free ones suffer from poor scalability or low efficiency, while gradient-based ones are often unavailable due to possibly non-differentiable controller structure. To resolve the issues, we tackle the controller tuning problem using a novel derivative-free reinforcement learning (RL) framework, which performs timestep-wise perturbation in parameter space during experience collection and integrates derivative-free policy updates into the advanced actor-critic RL architecture to achieve high versatility and efficiency. To demonstrate the framework's efficacy, we conduct numerical experiments on two concrete examples from autonomous driving, namely, adaptive cruise control with PID controller and trajectory tracking with MPC controller. Experimental results show that the proposed method outperforms popular baselines and highlight its strong potential for controller tuning.