Learning Transferable Cooperative Behavior in Multi-Agent Teams

This is the official repository of the 'Learning Transferable Cooperative Behavior in Multi-Agent Teams' paper available at https://arxiv.org/abs/1906.01202.

Dependencies

• Pytorch >=1.0
• MultiAgentParticleEnv (included in this repo)
• gym_vecenv
• tensorboardX

Installation

• Setup MAPE environment by running pip install -e . command inside the mape subdirectory.
• Setup gym_vecenv by executing pip install gym_vecenv.

Examples

See arguments.py file to see various command line arguments one can set while running scripts.

Normal Training

Training on a coverage control (simple_spread) environment can be started by running:

python main.py --env-name simple_spread --num-agents 3 --entity-mp --save-dir 0

Similarly scripts for formation control (simple_formation) and line control (simple_line) can be launched.

Curriculum training

To start curriculum training, specify the number of agents in automate.py file and execute the script:

python automate.py --env-name simple_spread --entity-mp --save-dir 0

Results

The models trained via curriculum learning on the three environments can be found in models subdirectory.

The corresponding results obtained from the trained policies are located in videos subdirectory.

You can also continue training on a saved model. For example, for training a team of 5 agents in simple_spread task from a policy trained with 3 agents, execute:

python main.py --env-name simple_spread --entity-mp --continue-training --load-dir models/ss/na3_uc.pt --num-agents 5

Contact

For any queries, feel free to raise an issue or contact the authors at [email protected] or [email protected].

License

This project is licensed under the MIT License.