Making Non-Stochastic Control (Almost) as Easy as Stochastic
Authors: Max Simchowitz
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithm is based on a novel variant of online Newton step [19], which adapts to the geometry induced by adversarial disturbances, and our analysis hinges on generic regret bounds for certain structured losses in the OCO-with-memory framework [6]. Theorem 3.1 (informal) When the agent knows the dynamics (1.1) (but does not have foreknowledge of disturbances nor the costs ℓt), DRC-ONS has Control Reg T = O( L2 poly(log T)). Theorem 3.2 (informal) When the dyamics are unknown, DRC-ONS with an initial estimation phase attains Control Reg T = e O( L2 T). All proofs are deferred to our appendix |
| Researcher Affiliation | Academia | Max Simchowitz EECS Department UC Berkeley Berkeley, CA 94720 msimchow@berkeley.edu |
| Pseudocode | Yes | Algorithm 1: Online Semi-Newton Step Semi-ONS(λ, η, C) |
| Open Source Code | No | The paper does not provide an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This theoretical paper does not conduct empirical studies with datasets, therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | This theoretical paper does not conduct empirical studies, therefore, no information about training/validation/test dataset splits is provided. |
| Hardware Specification | No | This theoretical paper does not conduct empirical experiments and therefore does not specify any hardware used. |
| Software Dependencies | No | This theoretical paper does not specify software dependencies with version numbers. |
| Experiment Setup | No | This theoretical paper does not conduct empirical experiments and therefore does not provide details on experimental setup like hyperparameters or training settings. |