Awesome

Libraries and versions

python 3.6.7, numpy 1.14.3, scipy 1.1.0, scikit-learn 0.19.1, torch 0.4.0, gym 0.10.9, matplotlib 1.5.1

Files included in this package

Decoding.py -- The decoding-based algorithm from this work.

Environments.py -- single library for loading all environments

Experiment.py -- entrypoint for experiments

GetSlopes.py -- postprocessing script for getting slope information on log-log plot.

LockBernoulli.py -- environment implementation for Lock-Bernoulli

LockGaussian.py -- environment implementation for Lock-Gaussian

OracleQ.py -- implementation of UCB-Q-Hoeffding from Jin et al. (2018)

Params.py -- wrapper infrastructure for experiments including hyperparameter configurations

Postprocess.py -- script for postprocessing data from experiments

PlotAll.py -- plotting script for comparisons

PlotSensitivity.py -- plotting script for sensitivity heatmap

QLearning.py -- implementation of Q-learning with epsilon-greedy exploration

Running the code

Make a directory called ./data/ from here. This will be where the files generated by the experiment script will appear.

The entry file is Experiments.py. This file takes a number of arguments, such as algorithm, environment, environment parameters, number of episodes, and any algorithm hyperparameters, conducts a simulation, and writes the running average reward into a file in the ./data directory. Please see that file for details on arguments.

Hyperparameter configurations used for sweeping are in Params.py. You may use this as follows:

import Params
for s in Parameters['Lock-v0']['oracleq']:
    P = Params.Params(s)
    P.iteration = 1
    print("python3 -W ignore Experiment.py %s" % (P.get_params_string()))

This code snippet will print all commands for the first replicate (iteration = 1) of OracleQ on Lock-Bernoulli.

Scripts for postprocessing and plotting data are contained in Postprocess.py, PlotAll.py, PlotSensitivity.py and GetSlopes.py

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.