Learning the Linear Quadratic Regulator from Nonlinear Observations
Authors: Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results constitute the first provable sample complexity guarantee for continuous control with an unknown nonlinearity in the system model. To our knowledge, this is the first polynomial-in-dimension sample complexity guarantee for continuous control with an unknown system nonlinearity and general function classes. |
| Researcher Affiliation | Collaboration | Zakaria Mhammedi ANU and Data61 EMAIL Dylan J. Foster MIT EMAIL Max Simchowitz UC Berkeley EMAIL Dipendra Misra Microsoft Research NYC EMAIL Wen Sun Microsoft Research NYC EMAIL Akshay Krishnamurthy Microsoft Research NYC EMAIL Alexander Rakhlin MIT EMAIL John Langford Microsoft Research NYC EMAIL |
| Pseudocode | Yes | Algorithm 1 Rich ID-CE |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code or direct links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on sample complexity guarantees for an algorithm. It does not mention the use of specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not discuss any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not discuss specific software dependencies with version numbers required for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |