State Aggregation Learning from Markov Transition Data
Authors: Yaqi Duan, Tracy Ke, Mengdi Wang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The application of our method to Manhattan traffic data successfully generates a data-driven state aggregation map with nice interpretations. ... We applied our method to a Manhattan taxi-trip dataset, with interesting discoveries. ... Simulation. We test our method on simulations (settings are in the appendix). The results are summarized in Figure 3. |
| Researcher Affiliation | Academia | Yaqi Duan Princeton University yaqid@princeton.edu Zheng Tracy Ke Harvard University zke@fas.harvard.edu Mengdi Wang Princeton University mengdiw@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Learning the Soft State Aggregation Model. |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We analyze a dataset of 1.1 × 107 New York city yellow cab trips that were collected in January 2016 [1]. [1] NYC Taxi and Limousine Commission (TLC) trip record data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed June 11, 2018. |
| Dataset Splits | No | The paper describes using a dataset but does not specify any training, validation, or test splits by percentage or sample count, nor does it refer to predefined splits with citations. |
| 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 using a 'vertex hunting algorithm in [21]' and 'feature-based RL [10]' but does not list any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper states that 'The algorithm takes as input 27 estimated aggregation distributions as state features' and 'We compute the optimal policy using feature-based RL [10]', but it does not provide specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed training configurations. |