Solving Continual Combinatorial Selection via Deep Reinforcement Learning
Authors: Hyungseok Song, Hyeryung Jang, Hai H. Tran, Se-eun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, Yung Yi
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various experiments demonstrate that our approach works well even when the item space is large and that it scales to environments with item spaces different from those used in training. |
| Researcher Affiliation | Collaboration | 1School of Electrical Engineering, KAIST, Daejeon, South Korea 2Informatics, King s College London, London, United Kingdom 3Naver Corporation, Seongnam, South Korea |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We refer the readers to our technical report3 for a more mathematical description. 3https://github.com/selectmdp |
| Open Datasets | No | The paper uses 'self-designed S-MDP environments (circle selection and selective predator-prey)' but provides no concrete access information (link, DOI, repository, or formal citation) for these datasets. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, processor types, or memory amounts) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states that 'all the details of the hyperparameters are provided in ourthe technical report', but these details are not present in the main text of the paper. |