Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments
Authors: Mahdi Imani, Seyede Fatemeh Ghoreishi, Ulisses M. Braga-Neto
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed framework is demonstrated using a simple dynamical system model with continuous state and action spaces, as well as a more complex model for a metastatic melanoma gene regulatory network observed through noisy synthetic gene expression data. |
| Researcher Affiliation | Academia | Mahdi Imani Texas A&M University College Station, TX, USA m.imani88@tamu.edu Seyede Fatemeh Ghoreishi Texas A&M University College Station, TX, USA f.ghoreishi88@tamu.edu Ulisses M. Braga-Neto Texas A&M University College Station, TX, USA ulisses@ece.tamu.edu |
| Pseudocode | Yes | Algorithm 1 Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | No | The paper uses "noisy synthetic gene expression data" and a "simple dynamical system model" but does not provide concrete access information or citations for a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) 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 | Yes | The decomposable squared exponential kernel function is used over the state and action spaces. The offline and MCMC sample sizes are 10 and 1000, respectively. The decomposable squared exponential and delta Kronecker kernel functions are used for Gaussian process regression over the belief state and action spaces, respectively. The offline and MCMC sample sizes are 10 and 3000, respectively. |