SYNC: SAFETY-AWARE NEURAL CONTROL FOR STABILIZING STOCHASTIC DELAY-DIFFERENTIAL EQUATIONS
Authors: Jingdong Zhang, Qunxi Zhu, Wei Yang, Wei Lin
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy of all the articulated control policies, including the SYNC, is demonstrated systematically by using representative control problems. ... In Figure 3, we numerically compare the NDC and a baseline... We show the results in Figure 4 and in Table 1 as well. Table 1 includes the training time (Tt), empirical energy cost E0.001, nearest distance (Nd) between the bicycle and target position, and empirical expectation E[τ0.001] for different methods. |
| Researcher Affiliation | Academia | Jingdong Zhang1,2, Qunxi Zhu2,*, Wei Yang2,3,*, Wei Lin1,2,3,4 1 School of Mathematical Sciences, SCMS, SCAM, and CCSB, Fudan University, Shanghai 200433, China 2 Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China 3 Shanghai Artificial Intelligence Laboratory, China 4 MOE Frontiers Center for Brain Science and State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200032, China {zhangjd20,qxzhu16,yangwei,wlin}@fudan.edu.cn |
| Pseudocode | Yes | In summary, the developed NDC framework is shown in Algorithm 1 in Appendix A.3.1. ... We summarize the framework in Algorithm 2 in Appendix A.3.1. |
| Open Source Code | Yes | We make all our code and data available at https://github.com/jingddong-zhang/SYNC. |
| Open Datasets | No | The paper mentions 'noise-perturbed kinematic bicycle model' and 'Chua s circuit' as systems used for experiments, but does not provide concrete access information (link, DOI, formal citation) for publicly available datasets used in the experiments. |
| Dataset Splits | No | The paper mentions training procedures and Monte Carlo sampling but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions providing code on GitHub, which implies software dependencies, but it does not explicitly list any specific software or library names with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The simulation configurations are described in Appendix A.3.4. ... We include more experimental details in Appendix A.3.5. ... experimental details are provided in Appendix A.3.6. |