Welcome to the Multi-Agent Tracking Environment’s documentation!
Welcome to the documentation of MATE, the Multi-Agent Tracking Environment.
The source code of the MultiAgentTracking environment is hosted on GitHub.
You can find it at mate.
For detailed description, please checkout our paper (PDF, bibtex).
This is an asymmetric two-team zero-sum stochastic game with partial observations, and each team has multiple agents (multiplayer). Intra-team communications are allowed, but inter-team communications are prohibited. It is cooperative among teammates, but it is competitive among teams (opponents).
Installation
git config --global core.symlinks true # required on Windows
pip3 install git+https://github.com/XuehaiPan/mate.git#egg=mate
Note
Python 3.7+ is required, and Python versions lower than 3.7 is not supported.
It is highly recommended to create a new isolated virtual environment for MATE using conda:
git clone https://github.com/XuehaiPan/mate.git && cd mate
conda env create --no-default-packages --file conda-recipes/basic.yaml # or full-cpu.yaml to install RLlib
conda activate mate
Contents:
- Getting Started
- The Environment Details
- Built-in Wrappers
- Repeated Reward and Individual Done
- Enhanced Observation
- Shared Field of View
- More Training Information
- Relative Coordinates
- Rescaled Observation
- Discrete Action Spaces
- Single-Team Multi-Agent Setting
- Single-Team Single-Agent Setting
- Auxiliary Camera Rewards
- Auxiliary Target Rewards
- Message Filter
- Random Message Dropout
- Restricted Communication Range
- No Communication
- Extra Communication Delays
- Render Communication
- Modules
- mate package
- Subpackages
- Submodules
- mate.constants module
TERRAIN_SIZETERRAIN_WIDTHTERRAIN_SPACEWAREHOUSESNUM_WAREHOUSESWAREHOUSE_RADIUSMAX_CAMERA_VIEWING_ANGLETARGET_RADIUSPRESERVED_SPACEPRESERVED_DIMOBSERVATION_OFFSETCAMERA_STATE_DIM_PUBLICCAMERA_STATE_SPACE_PUBLICCAMERA_STATE_DIM_PRIVATECAMERA_STATE_SPACE_PRIVATETARGET_STATE_DIM_PUBLICTARGET_STATE_SPACE_PUBLICTARGET_STATE_DIM_PRIVATETARGET_STATE_SPACE_PRIVATEOBSTACLE_STATE_DIMOBSTACLE_STATE_SPACECAMERA_ACTION_DIMCAMERA_DEFAULT_ACTIONTARGET_ACTION_DIMTARGET_DEFAULT_ACTIONcamera_observation_space_of()target_observation_space_of()observation_space_of()camera_observation_indices_of()target_observation_indices_of()observation_indices_of()camera_observation_slices_of()target_observation_slices_of()observation_slices_of()camera_coordinate_mask_of()target_coordinate_mask_of()coordinate_mask_of()
- mate.environment module
- mate.utils module
- Module contents
make()camera_observation_space_of()target_observation_space_of()observation_space_of()camera_observation_indices_of()target_observation_indices_of()observation_indices_of()camera_observation_slices_of()target_observation_slices_of()observation_slices_of()camera_coordinate_mask_of()target_coordinate_mask_of()coordinate_mask_of()read_config()EnvMetaMultiAgentTrackingEnhancedObservationSharedFieldOfViewRescaledObservationRelativeCoordinatesMoreTrainingInformationDiscreteCameraDiscreteTargetAuxiliaryCameraRewardsAuxiliaryTargetRewardsgroup_reset()group_step()group_observe()group_communicate()group_act()MultiCameraSingleCameraMultiTargetSingleTargetMessageFilterRestrictedCommunicationRangeRandomMessageDropoutNoCommunicationExtraCommunicationDelaysRenderCommunicationRepeatedRewardIndividualDoneWrapperMetaWrapperSpecCameraAgentBaseTargetAgentBaseRandomCameraAgentRandomTargetAgentNaiveCameraAgentNaiveTargetAgentGreedyCameraAgentGreedyTargetAgentHeuristicCameraAgentHeuristicTargetAgentMixtureCameraAgentMixtureTargetAgentconvert_coordinates()normalize_observation()rescale_observation()split_observation()CameraStatePublicCameraStatePrivateTargetStatePublicTargetStatePrivateObstacleStateseed_everything()sin_deg()cos_deg()tan_deg()arcsin_deg()arccos_deg()arctan2_deg()cartesian2polar()polar2cartesian()normalize_angle()Vector2DTeamMessage
- mate package
Citation
If you find MATE useful, please consider citing:
@inproceedings{pan2022mate,
title = {{MATE}: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control},
author = {Xuehai Pan and Mickel Liu and Fangwei Zhong and Yaodong Yang and Song-Chun Zhu and Yizhou Wang},
booktitle = {Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year = {2022},
url = {https://openreview.net/forum?id=SyoUVEyzJbE}
}