Source code for my Master's dissertation entitled Algorithm Selection for Subgraph Isomorphism Problems: A Reinforcement Learning Approach.
Running the scripts
To start, download and install R (version 3.4.4+) from CRAN. This installation contains the R interpreter and a simple GUI app for creating R scripts. This is sufficient to run the scripts in this repo; however, if you are planning to debug or modify the scripts, I highly suggest to use a full-featured IDE like RStudio.
Installing prerequisite packages
source('install_packages.R') on R command line to install all the necessary packages.
Rendering R Markdown (.Rmd) files
The rendered contents of the .Rmd files can be readily viewed at RPubs. Check out the following links:
These files can be rendered locally, and the easiest way to do this is through RStudio. Check out this guide.
- Algorithm Selection Library (aslib). RDoc | GitHub
- Leveraging Learning to Automatically Manage Algorithms (llama). RDoc | BitBucket
- R interface to TensorFlow. link
- Kotthoff, L. (2016). Algorithm selection for combinatorial search problems: A survey. In Data Mining and Constraint Programming (pp. 149-190). Springer, Cham. paper
- Kotthoff, L., McCreesh, C., & Solnon, C. (2016, May). Portfolios of subgraph isomorphism algorithms. In International Conference on Learning and Intelligent Optimization (pp. 107-122). Springer, Cham. paper
- Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., ... & Vanschoren, J. (2016). Aslib: A benchmark library for algorithm selection. Artificial Intelligence, 237, 41-58. paper
- Kotthoff, L. (2013). LLAMA: leveraging learning to automatically manage algorithms. arXiv preprint arXiv:1306.1031. paper
- Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2017, December). Open Algorithm Selection Challenge 2017: Setup and Scenarios. In Open Algorithm Selection Challenge 2017 (pp. 1-7). paper
- Smith-Miles, K. A. (2009). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys (CSUR), 41(1), 6. paper
- Sutton, R. S., & Barto, A. G. (1998). Introduction to reinforcement learning (Vol. 135). Cambridge: MIT press. book
- Policy Gradients