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.
To install the Gymjsp library, use pip install gymjsp
.
To use the Gymjsp library normally, you should install these Python libraries:
- gym
- networkx
- plotly
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()
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.