Measuring Structural Similarities in Finite MDPs
Authors: Hao Wang, Shaokang Dong, Ling Shao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our proposed similarity measures using a series of designed MDPs. |
| Researcher Affiliation | Collaboration | 1Inception Institute of Artificial Intelligence, UAE 2State Key Laboratory for Novel Software Technology, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 shows the iterative algorithm for computing σ S and σ A by simulating the recursion of Sec. 4.1. |
| Open Source Code | No | The paper does not mention or provide any links to open-source code for the described methodology. |
| Open Datasets | No | The paper states, 'We generate a series of n n grid MDPs {Mn}...'. This indicates a self-generated dataset for which no public access information (link, DOI, citation) is provided. |
| Dataset Splits | No | The paper describes experiments on generated MDPs but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions algorithms like 'Dijkstra’s algorithm' and 'successive shortest path (SSP) algorithm' but does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In all the experiments, we set CS = CA = 0.95 for our solution and c R = 0.05 and c T = 0.95 for dbis. ... Algorithm 1 calculates two similarity matrices, Sn and An, with parameters CS = CA = 0.95... We first fix CS = 0.95 and vary CA from 0.80 to 0.99 with a step-size of 0.01. Then, we swap the roles of CS and CA. |