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Author:
Technical University of Darmstadt, Institute for Production Management, Technology and Machine Tools (PTW)
Summary:
A modular framework for rolling-horizon operational control using a Gym-compatible agent–environment abstraction. The framework enables integration of optimization-based agents (Pyomo), reinforcement learning agents (Stable-Baselines3), heuristic search methods, and rule-based controllers. Environments can be instantiated from FMI-compliant simulations, mathematical models, or real-world system interfaces.
Latest version:
0.3.0
Required dependencies:
attrs
|
eta-nexus
|
fmpy
|
gymnasium
|
numpy
|
pandas
|
pydantic
|
pyomo
|
python-dateutil
|
pyyaml
|
stable-baselines3
|
tensorboard
|
tomli-w
|
torch
Optional dependencies:
autodoc-pydantic
|
cplex
|
keyboard
|
matplotlib
|
onnxruntime
|
pygame
|
pyglet
|
sphinx
|
sphinx-copybutton
|
sphinx-rtd-theme
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