Skip to content

README.md

Latest commit

 

History

History
27 lines (24 loc) · 1.35 KB

README.md

File metadata and controls

27 lines (24 loc) · 1.35 KB

RL4MicrostructureEvolution.png

RL4MicrostructureEvolution

Reinforcement Learning Environments for Structure Guided Process Optimization Tasks

Prerequisites

  • Download compiled microstructure-path simulation (uniax_simulator_for_microstructure_evolution_40tasks) and material model from https://fordatis.fraunhofer.de/handle/fordatis/201 and put to /msevolution_env/assets/sim
  • Intel Fortran environment to run the simulations and proper environment variables (eg. export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/<username>/intel/compilers_and_libraries_2019.3.199/linux/compiler/lib/intel64_lin:/home/<username>/intel/compilers_and_libraries_2019.3.199/linux/mkl/lib/intel64_lin)

Install and run microstructure-evolution environments

  • cd RL4MicrostructureEvolution
  • pip install .
  • cd msevolution_env/examples
  • python sg_random_agent.py for single-goal version or python meg_random_agent.py for multi-equivalent goal version

Cite

@article{dornheim2022deep,
  title={Deep reinforcement learning methods for structure-guided processing path optimization},
  author={Dornheim, Johannes and Morand, Lukas and Zeitvogel, Samuel and Iraki, Tarek and Link, Norbert and Helm, Dirk},
  journal={Journal of Intelligent Manufacturing},
  volume={33},
  number={1},
  pages={333--352},
  year={2022},
  publisher={Springer}
}