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Description:

pcgym 0.1.6

Reinforcement learning environments for process control

Quick start ⚡
Setup a CSTR environment with a setpoint change
import pcgym

# Simulation variables
nsteps = 100
T = 25

# Setpoint
SP = {'Ca': [0.85 for i in range(int(nsteps/2))] + [0.9 for i in range(int(nsteps/2))]}

# Action and observation Space
action_space = {'low': np.array([295]), 'high': np.array([302])}
observation_space = {'low': np.array([0.7,300,0.8]),'high': np.array([1,350,0.9])}

# Construct the environment parameter dictionary
env_params = {
'N': nsteps, # Number of time steps
'tsim':T, # Simulation Time
'SP' :SP,
'o_space' : observation_space,
'a_space' : action_space,
'x0': np.array([0.8, 330, 0.8]), # Initial conditions [Ca, T, Ca_SP]
'model': 'cstr_ode', # Select the model
}

# Create environment
env = pcgym.make_env(env_params)

# Reset the environment
obs, state = env.reset()

# Sample a random action
action = env.action_space.sample()

# Perform a step in the environment
obs, rew, done, term, info = env.step(action)

Documentation
You can read the full documentation here!
Installation ⏳
The latest pc-gym version can be installed from PyPI:
pip install pcgym

Examples
TODO: Link example notebooks here
Implemented Process Control Environments 🎛️

Environment
Reference
Source
Documentation

CSTR
Hedengren, 2022
Source

First Order Sytem
N/A
Source

Multistage Extraction Column
Ingham et al, 2007 (pg 471)
Source

Nonsmooth Control
Lim,1969
Source

Citing pc-gym
If you use pc-gym in your research, please cite using the following
@software{pcgym2024,
author = {Max Bloor and and Jose Neto and Ilya Sandoval and Max Mowbray and Akhil Ahmed and Mehmet Mercangoz and Calvin Tsay and Antonio Del Rio-Chanona},
title = {{pc-gym}: Reinforcement Learning Envionments for Process Control},
url = {https://github.com/MaximilianB2/pc-gym},
version = {0.0.4},
year = {2024},
}

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