To run Simulation I, provide the argument sim_1 to the main.py script. By default, when you run the script (main.py), it re-generates new datasets and runs the simulations on them. We provide the links to the generated datasets that we used at the bottom of this README file. However, to be able to repeat the simulations and also improve speed, we first generate the arm contexts, rewards, and other setup-related information and save them as HDF5, in the case of Simulation I, and pickled DataFrames, in the case of Simulations II & III. Moreover, none of the algorithms that we implement and test do offline-learning, thus there is no 'training' to be done. Once the library has been generated, place it both in the root directory where main.py is and also inside the tim_plus directory. Our code uses the TIM+ algorithm, for which you must link the C++ TIM+ code to Python.
We use the gpflow library for all GP-related computations and gpflow uses tensorflow.
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