This repository contains the code used for the experiments in "Estimating alpha-Rank by Maximising Information Gain". Paper is available here.
In particular it contains:
- An implementation of ResponseGraphUCB.
- A parallelized implementation of \alphaIG and \alphaWass.
- The X Good, Y Bad payoff games used in the paper.
To run experiments please use the run_experiments.py
file.
All hyper-parameters and settings are specified in run_experiments.py
.
In order to change the game used, please adjust the env_params
dictionary (the settings for 3 Good, 5 Bad are currently specified with the settings for 2 Good, 2 Bad and 4x4 Gaussian game being commented out).
The exp_params
list contains a list of algorithm configs (specified via dictionary) that will all be run.
If a list is specified as an argument, then all values in that list will be run (as is the case for \delta in ResponseGraphUCB).
The default hyper-parameters used for the 3 Good, 5 Bad experiments are currently used for the algorithms. Please change appropriately.
The notebook notebooks/graphing.ipynb
contains the code used for generating the regret graphs in the paper.
run_experiments.py
saves pickled dictionaries that contain all the information generated by a run.
Please see sampling.py
for the code that actually generates the data, and the exact meaning of each field.
All Python3 packages used are specified in requirements.txt
.
You will likely need install numpy before attempting to install ndd.
Additionally, a fortran compiler is required for ndd.
This can be installed on Ubuntu via gfortran (apt install gfortran
).
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