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ORSuite: Benchmarking Suite for Sequential Operations Models

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OR Suite

Reinforcement learning (RL) is a natural model for problems involving real-time sequential decision making. In these models, a principal interacts with a system having stochastic transitions and rewards and aims to control the system online (by exploring available actions using real-time feedback) or offline (by exploiting known properties of the system).

These project revolves around providing a unified landscape on scaling reinforcement learning algorithms to operations research domains.

Documentation

https://orsuite.readthedocs.io/en/latest/

Code Demonstration

https://colab.research.google.com/drive/1oSv8pCwl9efqU4VEHgi8KXNvHiPXi7r1?usp=sharing

Installation Guide

In order to install the required dpeendencies for a new conda environment, please run:

conda create --name ORSuite python=3.8.5
conda activate ORSuite
pip install -e .

High-Level Overview

The repository has three main components as a traditional Reinforcement Learning set-up :

  1. Environments : Environment for the agent to interact with and reside in. ~/or_suite/envs
  2. Agents : Choice of Algorithm ~/or_suite/agents
  3. Experiments : This is a take on implementing the enviroment and agents with a choice of algorithm ~/or_suite/experiment

Contribution Guide

See 'ORSuite Contribution Guide' to see information on how to add new environments and algorithms to the package.

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