Improved Strong Worst-case Upper Bounds for MDP Planning
Authors: Anchit Gupta, Shivaram Kalyanakrishnan
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We contribute to the theoretical analysis of MDP planning, which is the problem of computing an optimal policy for a given MDP. Specifically, we furnish improved strong worstcase upper bounds on the running time of MDP planning. |
| Researcher Affiliation | Academia | Anchit Gupta and Shivaram Kalyanakrishnan Department of Computer Science and Engineering, Indian Institute of Technology Bombay {anchit, shivaram}@cse.iitb.ac.in |
| Pseudocode | No | The paper describes algorithms verbally and mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |