Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
Authors: Edouard Leurent, Odalric-Ambrym Maillard, Denis Efimov
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 6 we demonstrate the applicability of Algorithm 1 in two numerical experiments: a simple illustrative example and a more challenging simulation for safe autonomous driving. We show in Table 1(a) the results of 100 simulations of a single episode: the robust agent performs worse than the nominal agent on average, but manages to ensure safety while the nominal agent collides with obstacles in 4% of simulations. |
| Researcher Affiliation | Collaboration | Edouard Leurent Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 CRISt AL, Renault F-59000 Lille, France edouard.leurent@inria.fr |
| Pseudocode | Yes | Algorithm 1 Robust Estimation, Prediction and Control |
| Open Source Code | Yes | Code and videos available at https://eleurent.github.io/robust-beyond-quadratic/. |
| Open Datasets | Yes | We consider the highway-env environment [25] for simulated driving decision problems. [25] Leurent, E. An environment for autonomous driving decision-making. https://github.com/ eleurent/highway-env, 2018. |
| Dataset Splits | No | The paper does not provide specific percentages, counts, or explicit methodology for training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) 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 | No | The paper describes general experimental settings like "K planning iterations" and "3000 episodes" for DQN, but does not provide specific hyperparameter values or detailed training configurations for the models. |