On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Authors: Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | However, we show that these popular decompositions for Conditional-Value-at-Risk (CVa R) and Entropic Value-at-Risk (EVa R) are inherently suboptimal regardless of the discretization level. In particular, we show that a saddle point property assumed to hold in prior literature may be violated. However, a decomposition does hold for Value-at-Risk and our proof demonstrates how this risk measure differs from CVa R and EVa R. |
| Researcher Affiliation | Collaboration | Jia Lin Hau University of New Hampshire Durham, NH jialin.hau@unh.edu Erick Delage HEC Montréal Montréal (Québec) erick.delage@hec.ca Mohammad Ghavamzadeh Amazon Palo Alto, CA ghavamza@amazon.com Marek Petrik University of New Hampshire Durham, NH mpetrik@cs.unh.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware, therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical analysis and proofs; therefore, it does not describe an experimental setup with specific hyperparameters or system-level training settings. |