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IMP-MARL (Digital twin): Integration of virtual sensing in the life-cycle management.

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IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL

IMP-MARL offers a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications.

In IMP-MARL, you can:

Additionally, you will be able to:

  • Retrieve the results of a benchmark campaign, where MARL methods are assessed in terms of scalability.
  • Reproduce our experiments.

This repository has been developed and is maintained by Pascal Leroy & Pablo G. Morato.

Please consider opening an issue or a pull request to help us improve this repository.

imp

Main requirements:

To work with our environments, one only needs to install Numpy.

However, to reproduce our results, more packages are required and installation instructions are provided here.

Tutorials

Sets of environments available:

Note: A campaign cost can be activated in any environment.

Available wrappers with examples:

PyMarl algorithms available:

To train agents with PyMarl and one of the following algorithms, instructions are available here:

The main code is derived from PyMarl original implementation.

Expert-knowledge baselines available:

Run an IMP environment

env = Struct({'n_comp': 3,
               'discount_reward': 0.95,
               'k_comp': 2,
               'env_correlation': False,
               'campaign_cost': False})

obs, rewards_sum, done = env.reset(), 0, False
while not done:
    actions = {f"agent_{i}": random.randint(0,2) for i in range(3)}
    obs, rewards, done, insp_outcomes = env.step(actions) 

Citation

If you use IMP-MARL in your work, please consider citing our paper:

@misc{leroy2023impmarl,
      title={IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL}, 
      author={Pascal Leroy and Pablo G. Morato and Jonathan Pisane and Athanasios Kolios and Damien Ernst},
      year={2023},
      eprint={2306.11551},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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