A Functional Learning Approach for Team-Optimal Traffic Coordination
This work addresses traffic coordination for multi-agent systems, but it appears incremental as it builds on existing Hilbert space and policy iteration methods.
The paper tackles the problem of computing team-optimal strategies for traffic coordination by developing a kernel-based policy iteration functional learning framework, and it demonstrates the method's effectiveness in signal-free intersection scenarios through simulations in SUMO.
In this paper, we develop a kernel-based policy iteration functional learning framework for computing team-optimal strategies in traffic coordination problems. We consider a multi-agent discrete-time linear system with a cost function that combines quadratic regulation terms and nonlinear safety penalties. Building on the Hilbert space formulation of offline receding-horizon policy iteration, we seek approximate solutions within a reproducing kernel Hilbert space, where the policy improvement step is implemented via a discrete Fréchet derivative. We further study the model-free receding-horizon scenario, where the system dynamics are estimated using recursive least squares, followed by updating the policy using rolling online data. The proposed method is tested in signal-free intersection scenarios via both model-based and model-free simulations and validated in SUMO.