Solving Uncertain MDPs by Reusing State Information and Plans
Authors: Ping Hou, William Yeoh, Tran Cao Son
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our approach is typically faster than replanning from scratch and we also provide a way to estimate the amount of speedup based on the amount of information being reused. |
| Researcher Affiliation | Academia | Ping Hou Department of Computer Science New Mexico State University Las Cruces, NM 88003 phou@cs.nmsu.edu William Yeoh Department of Computer Science New Mexico State University Las Cruces, NM 88003 wyeoh@cs.nmsu.edu Tran Cao Son Department of Computer Science New Mexico State University Las Cruces, NM 88003 tson@cs.nmsu.edu |
| Pseudocode | Yes | Algorithm 1: INCREMENTAL-MDP(M) and Algorithm 2: FIND-REUSABLE-STATES(I, S , Y ) |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We run the algorithms on two sets of domains: (i) a multi-layer navigation domain similar to that in the literature (Dai et al. 2011), and (ii) the domains in the ICAPS 2011 International Probabilistic Planning Competition (IPPC).4 |
| Dataset Splits | No | The paper describes experimental setups and problem generation for the domains used but does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | We conducted our experiments on a dual-core 2.53 GHz machine with 4GB of RAM. |
| Software Dependencies | No | The paper mentions the MDP algorithms used (e.g., VI, TVI, UCT) but does not specify any software dependencies or their version numbers (e.g., Python, PyTorch, specific solver versions). |
| Experiment Setup | Yes | We set X to 50, Y to 500, and R to 50. |