Official code for the paper "Toward multi-target self-organizing pursuit in a partially observable Markov game", which has been submitted to Arxiv and journal for peer-review.
Using the following to cite:
Sun, L., Chang, Y.C., Lyu, C., Shi, Y., Shi, Y. and Lin, C.T., 2022. Toward multi-target self-organizing pursuit in a partially observable Markov game. arXiv preprint arXiv:2206.12330.
In the proposed FSC2, the multi-target self-organizing pursuit (MTSOP or SOP) problem is decomposed into three subtasks: fuzzy-based distributed task allocation (DTA), self-organizing search (SOS), and single-target pursuit (STP).
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The MTSOP, i.e, the proposed FSC2 algorithm, is in the folder multi_target_self_organizing_pursuit.
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The SOS task is trained and tested in the folder multi_target_self_organizing_search.
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The proposed global distributed consistency (DC) metric in task allocation is in the folder fuzzy_clustering_metric.
Python 3.7.11
numpy 1.19.1
torch 1.10.2
mpi4py 3.1.3
To run the comparison code of ApeX-DQN, additional dependencies are:
tensorflow 1.15.0
ray 1.10.0
To run the comparison code of MADDPG, additional dependencies are:
https://github.com/openai/multiagent-particle-envs
All the dependencies are listed in the file environment_for_mtsop_fsc2.yml.
The actor-critic codes are mostly from and modified based on
The ApeX-DQN codes for comparison are from
- https://github.com/aecgames-paper/aecgames
- https://github.com/parametersharingmadrl/parametersharingmadrl
The MADDPG codes for comparison are from