Real-World Multi-Agent Reinforcement Learning problems, potentially yielding a high positive impact on society when solved.
Project description
HiveX
Real-World Multi-Agent Reinforcement Learning problems, potentially yielding a high positive impact on society when solved.
About
The motivation of the HiveX suite is to provide advanced reinforcement learning benchmarking environments with an emphasis on: (1) real-world scenarios, (2) multi-agent systems, (3) investigating problems and solutions with high impact on society, (4) cooperation and communication mechanisms.
Available Environments
Thumbnail | Title | Domain | Paper | Env Name |
---|---|---|---|---|
Windfarm Orientation Optimisation | Distributed Energy Grids | Link (Neurips'21) |
"WindFarm" |
|
Wildfire-Management Resource Distribution |
Catastrophe Management | Link (ICLR'22) |
"Wildfire" |
Installation
hivex
can be installed from PyPI using pip
:
pip install hivex
Manual install using conda virtual environment
The installation steps are as follows:
-
Create and activate a virtual environment, e.g.:
conda create -n hivex python=3.9 conda activate hivex
-
Install
hivex
:git clone -b main https://github.com/philippds/HiveX cd HiveX pip install .
-
Test the
hivex
installation:cd <hivex_root>/hivex/python/pytest pytest test.py
Example Usage
Training agents with UnityEnvironment (Environment Interface | Learning Framework)
UnityEnvironment | Random
cd <hivex_root>/examples/UnityEnvironment
python UnityEnvironment_train.py
Training agents with ML-Agents (Environment Interface | Learning Framework)
UnityEnvironment | ML-Agents
cd <hivex_root>/examples/ml_agents
mlagents-learn config/WindFarm.yaml --env=<hivex_root>/hivex/environments/hivex_WindFarm_x86_64 --run-id="WindFarm_Test_01"
Training agents with Stable-Baselines3 (Environment Interface | Learning Framework)
Stable-Baselines3 VecEnv | Stable-Baselines3
cd <hivex_root>
pip install -e .[stable-baselines3]
cd <hivex_root>/examples/stable_baselines3
python VecEnv_train.py
Gym | Stable-Baselines3
cd <hivex_root>/examples/gym
python gym_train.py
dm_env | Random
cd <hivex_root>/examples/dm_env
python dm_env_train.py
Interface Test (Optional)
cd <hivex_root>/examples/dm_env/utilities
python dm_env_test.py
Training agents with RLLib (Linux Only) (Environment Interface | Learning Framework)
cd <hivex_root>
pip install -e .[rllib]
pip install -e .[pettingzoo]
PettingZoo ParallelEnv (AEC) | RLLib
cd <hivex_root>/examples/pettingzoo_ParallelEnv
python ParallelEnv_train.py
Interface Test (Optional)
cd <hivex_root>/examples/pettingzoo_ParallelEnv/utilities
python api_test.py
python parallel_api_test.py
Documentation
Full documentation is available here
Citing HiveX
If you use HiveX in your work, please cite:
@inproceedings{siedler2022hivex,
title={},
author={Philipp D. Siedler},
year={2022},
journal={},
organization={}
}
Project details
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