Recommend to run code within a conda virtual environment. Create a virtual environment by
conda create -n dicg python=3.7
Activate the virtual environment by running
conda activate dicg
Install dependencies by running the following command in the root directory of this repo (in the virtual environment):
pip install -r requirements.txt
The predator-prey environment and the traffic junction environment are included in this repo. To run the StarCraft Multi-agent Challenge (SMAC) environment, please follow the instructions here. You will need to install the full StarCraft II game as well as the SMAC maps.
Experiment runners are in /exp_runners/predatorprey/, detailed argument specification list can be found in the included runner files (e.g. detailed environment size, number of agents, network sizes, algorithm hyperparemters, etc.).
To train in Predator-Prey, please cd into /exp_runners/predatorprey/ and run following commands by replacing penalty P with desired values (0, -1, -1.25 or -1.5):
| Approach | Command |
|---|---|
| DICG-CE-MLP | python dicg_ce_predatorprey_runner.py --penalty P |
| DICG-DE-MLP | python dicg_de_predatorprey_runner.py --penalty P |
| CENT-MLP | python cent_predatorprey_runner.py --penalty P |
| DEC-MLP | python dec_predatorprey_runner.py --penalty P |
The model checkpoints will be saved in /exp_runners/predatorprey/data/local/.
Reported average return (average of 5 random seeds, utill 7.5e6 environment steps):
| Approach | P = 0 |
P = -1 |
P = -1.25 |
P = -1.5 |
|---|---|---|---|---|
| DICG-CE-MLP | 78.6 ± 0.1 | 70.2 ± 0.7 | 72.0 ± 1.0 | 72.0 ± 0.7 |
| DICG-DE-MLP | 78.2 ± 0.0 | 70.5 ± 2.7 | 72.4 ± 1.1 | 70.0 ± 2.7 |
| CENT-MLP | 75.0 ± 0.1 | -39.8 ± 3.8 | -52.28 ± 1.3 | -65.8 ± 2.9 |
| DEC-MLP | 74.4 ± 0.3 | 56.6 ± 0.7 | 56.7 ± 1.1 | 57.9 ± 0.3 |
Experiment runners are in /exp_runners/smac/, detailed argument specification list can be found in the included runner files.
To train in SMAC, please cd into /exp_runners/smac/ and run following commands by replacing MAP with desired scenario strings (8m_vs_9m, 3s_vs_5z or 6h_vs_8z):
| Approach | Command |
|---|---|
| DICG-CE-LSTM | python dicg_ce_smac_runner.py --map MAP |
| DICG-DE-LSTM | python dicg_de_smac_runner.py --map MAP |
| CENT-LSTM | python cent_smac_runner.py --map MAP |
| DEC-LSTM | python dec_smac_runner.py --map MAP |
The model checkpoints will be saved in /exp_runners/smac/data/local/.
Reported average win rate (average of 5 random seeds, utill 9e6 environment steps for 8m_vs_9m and 1.8e7 environment steps for the other two maps):
| Approach | 8m_vs_9m |
3s_vs_5z |
6h_vs_8z |
|---|---|---|---|
| DICG-CE-LSTM | 72 ± 11 % | 96 ± 3 % | 9 ± 9 % |
| DICG-DE-LSTM | 87 ± 6 % | 99 ± 1 % | 0 |
| CENT-LSTM | 42 ± 6 % | 0 | 0 |
| DEC-LSTM | 65 ± 16 % | 94 ± 5 % | 0 |
Experiment runners are in /exp_runners/traffic/, detailed argument specification list can be found in the included runner files.
To train in Traffic Junction, please cd into /exp_runners/traffic/ and run following commands by replacing difficulty D with desired setting strings (easy, medium or hard):
| Approach | Command |
|---|---|
| DICG-CE-MLP | python dicg_ce_traffic_runner.py --difficulty D |
| DICG-DE-MLP | python dicg_de_traffic_runner.py --difficulty D |
| CENT-MLP | python cent_traffic_runner.py --difficulty D |
| DEC-MLP | python dec_traffic_runner.py --difficulty D |
The model checkpoints will be saved in /exp_runners/traffic/data/local/.
Reported average success rate (average of 5 random seeds, utill 2.5e6 environment steps):
| Approach | easy |
medium |
hard |
|---|---|---|---|
| DICG-CE-MLP | 98.1 ± 1.9 % | 80.5 ± 6.8 % | 22.8 ± 4.6 % |
| DICG-DE-MLP | 95.6 ± 1.5 % | 90.8 ± 2.9 % | 82.2 ± 6.0 % |
| CENT-MLP | 97.7 ± 0.9 % | 0 | 0 |
| DEC-MLP | 90.2 ± 6.5 % | 81.3 ± 4.8 % | 69.4 ± 4.9 % |
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