We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| KehanLong/DR_Stabilizing_Policy | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| generate_uncertainty_samples | Ran | KehanLong/DR_Stabilizing_Policy/DR_LF_Learning/Inverted_pendulum_learning.py code served (permissive licence) · get_code("36174d495abcc26c") |
| generate_uncertainty_samples | Ran | KehanLong/DR_Stabilizing_Policy/DR_LF_Learning/Mountain_car_learning.py code served (permissive licence) · get_code("82101714f74c7399") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. A key challenge in designing controllers with stability guarantees for uncertain systems is the accurate determination of and adaptation to shifts in model parametric uncertainty during online deployment. We tackle this with a novel distributionally robust formulation of the Lyapunov derivative chance constraint ensuring a monotonic decrease of the Lyapunov certificate. To avoid the computational complexity involved in dealing with the space of probability measures, we identify a sufficient condition in the form of deterministic convex constraints that ensures the Lyapunov derivative constraint is satisfied. We integrate this condition into a loss function for training a neural network-based controller and show that, for the resulting closed-loop system, the global asymptotic stability of its equilibrium can be certified with high confidence, even with Out-of-Distribution (OoD) model uncertainties. To demonstrate the efficacy and efficiency of the proposed methodology, we compare it with an uncertainty-agnostic baseline approach and several reinforcement learning approaches in two control problems in simulation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.03017")
get_code_for_paper("2404.03017")
have("2404.03017")
Connect an agent — have() is free.