Disentangling Linear Quadratic Control with Untrusted ML Predictions
Authors: Tongxin Li, Hao Liu, Yisong Yue
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the applicability of DISC across a spectrum of practical scenarios. We demonstrate the practicality of DISC through two real-world examples (Section 5): a drone navigation problem with mixed external disturbances and voltage control in a power grid with heterogeneous power injections. |
| Researcher Affiliation | Academia | Tongxin Li1 Hao Liu2 Yisong Yue2 1School of Data Science The Chinese University of Hong Kong, Shenzhen, China litongxin@cuhk.edu.cn 2Computing + Mathematical Sciences California Institute of Technology, Pasadena, USA {hliu3, yyue}@caltech.edu |
| Pseudocode | Yes | Algorithm 1: DISentangled Confidence (DISC) policy |
| Open Source Code | Yes | The code is available at https://github.com/tspbfs/Disentangle Control. |
| Open Datasets | Yes | For the regular solar and wind generation time series, we use the real solar PV generation time series data from the DTU-Data in 2021 [32] and wind generation from the U.S. Virgin Islands Wind Resources from the National Renewable Energy Lab (NREL) [33]. |
| Dataset Splits | No | The paper specifies a |
| Hardware Specification | No | Our contributions focus on the theory side, and the experimental setup is sufficiently basic that it does not require intense computing resources such as GPUs. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | Experimental Setup. To generate ML predictions, we use the Fast ICA method in [30] to decompose the mixed perturbations and train a multi-layer perceptron neural network with 4 hidden layers to predict the future latent variables. ... we implement a follow-the-regularized-leader (FTRL) optimization with a ℓ2-regularizer... The detailed parameters and hyper-parameters used in our experiments can be found in Appendix C.3. ... The warm-start buffer size is set as 100 and 50 respectively for the drone navigation and voltage control applications. ... we update the ML prediction model at each time step t [T] and use a length-b subsequence of the collected time series... to predict the future w = 5 (w is the prediction window size defined in Section 2) steps of perturbations... All prediction models are formed via a fully-connected neural network with 4 hidden layers with a width 80 and Leaky Re LU as the activation function except the final layer, and are trained using Adam [72] as the optimizer with a learning rate 1e 3 for 500 epochs. ... Table 2 below summarizes the detailed parameters used in Section C. |