Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes
Authors: Yifan Lin, Enlu Zhou
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate the empirical performance of the BR-MDP formulation and the proposed algorithm. We verify the theoretical results and demonstrate the performance of our algorithms via numerical experiments in Section 4. |
| Researcher Affiliation | Academia | Yifan Lin, Enlu Zhou Industrial and Systems Engineering Georgia Institute of Technology 755 Ferst Dr Atlanta, GA 30332 USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Approximate Bilevel DCP, Algorithm 2: New Posterior Set Generation, Algorithm 3: ABDCP for infinite-horizon BR-MDPs |
| Open Source Code | No | The information is insufficient. The paper does not contain any explicit statement about providing source code or a link to a code repository for the described methodology. It mentions existing methods and their code, but not its own. |
| Open Datasets | No | The information is insufficient. The paper describes experiments on an 'offline path planning problem' using 'independent datasets' that are 'randomly sampled from the true distribution,' but it does not specify any publicly available datasets, nor does it provide links, DOIs, or citations for accessing the data used. |
| Dataset Splits | No | The information is insufficient. The paper mentions using different 'data size N = 10 and N = 1000' and running '200 replications on different independent datasets', but it does not provide specific details on how these datasets were split into training, validation, or test sets. |
| Hardware Specification | No | The information is insufficient. The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The information is insufficient. The paper does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the implementation of their algorithms. |
| Experiment Setup | No | The information is insufficient. While the paper describes numerical experiments for an 'offline path planning problem' with varying 'data size N = 10 and N = 1000' and different 'risk levels α' for CVaR, it does not provide specific hyperparameter values, model initialization details, or other explicit training configurations that would be needed for reproducibility. |