Share a benchmark that can easily apply reinforcement learning in Job-shop-scheduling

Gymjsp

Gymjsp is an open source Python library, which uses the OpenAI Gym interface for easily instantiating and interacting with RL environments, and the OR-Library which is a collection of test data sets for a variety of Operations Research (OR) problems(i.e., job shop schedule, portfolio optimisation and so on). In this Python library, we only use the job shop schedule instances in OR-Library.

Installation

To install the Gymjsp library, use pip install gymjsp.

Dependency

To use the Gymjsp library normally, you should install these Python libraries:

• gym
• networkx
• plotly

API

The Gymjsp API's API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple- here's an example using the "ft06" instance environment:

from gymjsp import BasicJsspEnv
env = BasicJsspEnv('ft06')

# env is created, now we can use it: 
for episode in range(10): 
    obs = env.reset()
    for step in range(50):
        action = env.action_space.sample()  # or given a custom model, action = policy(observation)
        nobs, reward, done, info = env.step(action)
        
env.render()

Release Notes

There used to be release notes for all the new Gymjsp versions here. New release notes are being moved to releases page on GitHub, like most other libraries do.