An optimized RL approach to learn and simulate the Job-Shop Scheduling problem using Constraint Programming
Project description
A Constraint Programming Based Job-Shop Scheduling Environment
This is a Constraint Programming (CP) based Job-Shop Scheduling (JSS) Environment that can be combined with Reinforcement-Learning (RL) to train an agent to solve a scheduling problem.
Compared to other environments, this CP based one is made to be fast and scalable.
If you install it using setup.py
, it will automatically compile using MyPyC.
Also, this environment has been developed for an end-to-end approach, there is no pre-defined reward function and the observation are simply the raw IntervalVariable
representation.
We recommend the reader to check the paper for more information.
If you need to define a reward function or a different observation, you can do so by forking the environment and modifying the step
function.
Installation
pip install jss_cp
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