Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Online Nonstochastic Control with Adversarial and Static Constraints
Authors: Xin Liu, Zixian Yang, Lei Ying
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiment In this section, we test our algorithms on a quadrotor vertical flight (QVF) control under an adversarial environment, which is modified from (Li et al., 2023). ... Figure 1 shows the experiment results for QVT control with winds blowing down wt U( 5.5, 4.5). |
| Researcher Affiliation | Academia | 1The School of Information Science and Technology, Shanghai Tech University, Shanghai, China. 2The Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor, Ann Arbor, USA. |
| Pseudocode | Yes | Constrained Online Nonstochastic Control Algorithm Initialize: a (Îș, Ï) stable controller K and the proper learning rates in COCO-Solver. for t = 1, , T, do Observe state xt and compute the disturbance wt 1. Apply control ut = Kxt + PH i=1 M[i] t wt i. Receive feedback including cost function ct(xt, ut) and constraint functions dt(xt, ut) and l(xt, ut). Compute the approximated cost function ct( ) and constraint functions dt( ) and l( ). Invoke the COCO-Solver(Mt, Qt, ct( ), dt( ), l( )) to obtain Mt+1 and Qt+1. end for |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | No | The paper describes experiments on 'quadrotor vertical flight (QVF) control' and 'Heating Ventilation and Air Conditioning (HVAC) control', which are simulated environments or control systems rather than publicly available datasets with specific access information. |
| Dataset Splits | No | The paper describes control system simulations and experiments but does not provide specific dataset split information (e.g., percentages or counts for training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Let m = 1kg, g = 9.8m/s2, and Ia = 0.25kg/s. The system is discretized with t = 1s. We impose time-varying constraints, zt 0.3 + 0.3 sin(t/10), to emulate the complicated time-varying obstacles on the ground. The static affine constraints are zt 1.7 and 0 vt 12. We consider a time-varying quadratic cost function 0.1(zt 0.7)2 + 0.1 z2 t + Ït(vt 9.8)2, where Ït U(0.1, 0.2). We simulate two different wind conditions wt U( 5.5, 4.5) (winds blow down) and wt U(4.5, 5.5) (winds blow up), respectively. |