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The source code for the paper: 'ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems'

License: MIT No Attribution

Shell 2.60% Python 80.53% Jupyter Notebook 16.70% Dockerfile 0.17%

or-rl-benchmarks's Introduction

OR Rl Benchmarks

The source code for the paper: 'ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems'

AWS SageMaker Setup

  1. Login to your AWS account.
  2. Search for SageMaker
  3. Click Notebook instances
  4. Enter notebook name of your choice โ†’ choose instance type ml.t2.medium
  5. Create a new IAM role if required. Also attach AmazonEC2ContainerRegistryPowerUser policy to the role. See guide for adding permissions to an IAM Role.
  6. Under Git repositories, click Additional repository โ†’ Select Clone a Public repo and enter https://github.com/awslabs/or-rl-benchmarks.git
  7. Click Create Notebook and wait for the instance to come up.
  8. Once the instance is ready, select it and click Open Jupyter
  9. Above step will load this repository and then you can follow the python notebooks for each problem.

Contributors

This is a joint work of -

Bharathan Balaji @bbalaji-ucsd

Jordan Bell-Masterson

Enes Bilgin

Andreas Damianou @adamian

Pablo Moreno Garcia

Arpit Jain @arpitjain2811

Runfei Luo @annaluo676

Alvaro Maggiar

Balakrishnan Narayanaswamy

Chun Ye

License Summary

This sample code is made available under the MIT-0 license. See the LICENSE file.

or-rl-benchmarks's People

Contributors

hyandell avatar arpitjain2811 avatar

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