Akshat Agarwal*, Sumit Kumar*, Katia Sycara
Robotics Institute, Carnegie Mellon University
This is the official repository of the 'Learning Transferable Cooperative Behavior in Multi-Agent Teams' paper, available at https://arxiv.org/abs/1906.01202
This work has been presented at the Learning and Reasoning with Graph-Structured Reprsentations workshop (https://graphreason.github.io/papers/29.pdf) at ICML, 2019 held in Long Beach, USA.
See requirements.txt file for the list of dependencies. Create a virtualenv with python 3.5 and setup everything by executing pip install -r requirements.txt.
See arguments.py file for the list of various command line arguments one can set while running scripts.
Training on 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 as:
python main.py --env-name simple_formation --num-agents 3 --save-dir 0
python main.py --env-name simple_line --num-agents 3 --save-dir 0
Specify the flag --test if you do not want to save anything.
To start curriculum training, specify the number of agents in automate.py file and execute:
python automate.py --env-name simple_spread --entity-mp --save-dir 0
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 from 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
For any queries, feel free to raise an issue or contact the authors at [email protected] or [email protected].
This project is licensed under the MIT License.