Policy Learning for Continuous Space Security Games Using Neural Networks
Authors: Nitin Kamra, Umang Gupta, Fei Fang, Yan Liu, Milind Tambe
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the potential to predict good defender strategies via experiments and analysis of Opt Grad FP and Opt Grad FP-NN on discrete and continuous game settings. |
| Researcher Affiliation | Academia | University of Southern California1, Carnegie Mellon University2 {nkamra, umanggup, yanliu.cs, tambe}@usc.edu1, feifang@cmu.edu2 |
| Pseudocode | Yes | Algorithm 1: Opt Grad FP |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper describes using "1000 randomly generated forest states" for training but does not provide any specific access information (link, DOI, repository, or citation) to make this dataset publicly available or reproducible. |
| Dataset Splits | No | The paper mentions training on "1000 randomly generated forest states" and testing on "10 new forest states", indicating a train/test split, but it does not specify a validation set or precise percentage/counts for these splits, nor does it refer to predefined splits for these custom generated states. |
| Hardware Specification | No | The paper does not specify the exact hardware used for running experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions software components like convolutional neural networks and policy gradients, but it does not provide specific version numbers for any programming languages, libraries, or frameworks (e.g., Python, TensorFlow, PyTorch, CUDA) used in the implementation. |
| Experiment Setup | Yes | The forest game s hyperparameters for the single forest state case are summarized in Table 4. Opt Grad FP-NN for multiple forest states uses the same parameters except epmax = 20000 and E = 500000. The architectures of all neural networks presented earlier and all algorithm hyperparameters were chosen by doing informal grid searches within appropriate intervals. |