Probabilistic Planning with Risk-Sensitive Criterion
Authors: Ping Hou
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In my work, I introduced various algorithms for RSMDPs with different assumptions... In our recent paper (Hou, Yeoh, and Varakantham 2014), we formally defined Risk-Sensitive MDPs (RS-MDPs) and show that the optimal policy for RS-MDPs is not stationary in the original state space... The overall scope of my thesis is to develop efficient and scalable algorithms to optimize the RS-criterion in probabilistic planning problems. |
| Researcher Affiliation | Academia | Ping Hou Department of Computer Science New Mexico State University Las Cruces, NM 88003, USA phou@cs.nmsu.edu |
| Pseudocode | No | The paper describes several algorithms (TVI-DP, TVI-DFS, PVI-DP, PO-FVI) but does not provide their pseudocode or formal algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements regarding the availability of source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and focuses on algorithm design and research plans. It does not describe experiments that use a specific dataset, nor does it provide access information for any dataset. |
| Dataset Splits | No | This paper is theoretical and outlines algorithm design and future research. It does not report on empirical experiments or specify any training, validation, or test dataset splits. |
| Hardware Specification | No | This paper describes theoretical algorithms and research plans. It does not report on empirical experiments and therefore does not specify any hardware used. |
| Software Dependencies | No | This paper focuses on theoretical algorithm design and research plans. It does not report on empirical experiments and therefore does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical, outlining algorithm design and future research plans. It does not report on empirical experiments or provide specific experimental setup details such as hyperparameters or training configurations. |