ZhangNYG/IC3Net

Code for ICLR 2019 paper: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

IC3Net

This repository contains reference implementation for IC3Net paper (accepted to ICLR 2019), Learning when to communicate at scale in multiagent cooperative and competitive tasks, available at https://arxiv.org/abs/1812.09755

Cite

If you use this code or IC3Net in your work, please cite the following:

@article{singh2018learning,
  title={Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks},
  author={Singh, Amanpreet and Jain, Tushar and Sukhbaatar, Sainbayar},
  journal={arXiv preprint arXiv:1812.09755},
  year={2018}
}

Standalone environment version

• Find gym-starcraft at this repository: apsdehal/gym-starcraft
• Find ic3net-envs at this repository: apsdehal/ic3net-envs

Installation

First, clone the repo and install ic3net-envs which contains implementation for Predator-Prey and Traffic-Junction

git clone https://github.com/IC3Net/IC3Net
cd IC3Net/ic3net-envs
python setup.py develop

Optional: If you want to run experiments on StarCraft, install the gym-starcraft package included in this package. Follow the instructions provided in README inside that packages.

Next, we need to install dependencies for IC3Net including PyTorch. For doing that run:

pip install -r requirements.txt

Running

Once everything is installed, we can run the using these example commands

Note: We performed our experiments on nprocesses set to 16, you can change it according to your machine, but the plots may vary.

Note: Use OMP_NUM_THREADS=1 to limit the number of threads spawned

Predator-Prey

• IC3Net on easy version

python main.py --env_name predator_prey --nagents 3 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 5 --max_steps 20 --ic3net --vision 0 --recurrent

• CommNet on easy version

python main.py --env_name predator_prey --nagents 3 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 5 --max_steps 20 --commnet --vision 0 --recurrent

• IC on easy version

python main.py --env_name predator_prey --nagents 3 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 5 --max_steps 20 --vision 0 --recurrent

• IRIC on easy version

python main.py --env_name predator_prey --nagents 3 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 5 --max_steps 20 --mean_ratio 0 --vision 0 --recurrent

For medium version, change the following arguments:

• nagents to 5
• max_steps to 40
• vision to 1
• dim to 10

For hard version, change the following arguments:

• nagents to 10
• max_steps to 80
• vision to 1
• dim to 20

Traffic Junction

• IC3Net on easy version

python main.py --env_name traffic_junction --nagents 5 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 6 --max_steps 20 --ic3net --vision 0 --recurrent --add_rate_min 0.1 --add_rate_max 0.3 --curr_start 250 --curr_end 1250 --difficulty easy

• CommNet on easy version

python main.py --env_name predator_prey --nagents 5 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 6 --max_steps 20 --commnet --vision 0 --recurrent  --add_rate_min 0.1 --add_rate_max 0.3 --curr_start 250 --curr_end 1250 --difficulty easy

• IC on easy version

python main.py --env_name predator_prey --nagents 5 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 6 --max_steps 20 --vision 0 --recurrent  --add_rate_min 0.1 --add_rate_max 0.3 --curr_start 250 --curr_end 1250 --difficulty easy

• IRIC on easy version

python main.py --env_name predator_prey --nagents 5 --nprocesses 16 --num_epochs 2000 --hid_size 128 --detach_gap 10 --lrate 0.001 --dim 6 --max_steps 20 --mean_ratio 0 --vision 0 --recurrent --add_rate_min 0.1 --add_rate_max 0.3 --curr_start 250 --curr_end 1250 --difficulty easy

For medium version, change the following arguments:

• nagents to 10
• max_steps to 40
• dim to 14
• add_rate_min to 0.05
• add_rate_max to 0.02
• difficulty to medium

For hard version, change the following arguments:

• nagents to 20
• max_steps to 80
• dim to 18
• add_rate_min to 0.02
• add_rate_max to 0.05
• difficulty to hard

StarCraft

Make sure you have gym-starcraft properly installed and configuration properly configured.

For explore task 50x50, 10Medic, see the examples below, replace torchcraft_dir argument with your torchcraft directory location

• IC3Net

python -u main.py --env_name starcraft --task_type explore --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 1 --our_unit_type 34 --enemy_unit_type 34 --init_range_end 150 --ic3net --recurrent --rnn_type LSTM --detach_gap 10 --stay_near_enemy --explore_vision 10 --step_size 16

• CommNet

python -u main.py --env_name starcraft --task_type explore --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 1 --our_unit_type 34 --enemy_unit_type 34 --init_range_end 150 --commnet --recurrent --rnn_type LSTM --detach_gap 10 --stay_near_enemy --explore_vision 10 --step_size 16

• IRIC

python -u main.py --env_name starcraft --task_type explore --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 1 --our_unit_type 34 --enemy_unit_type 34 --init_range_end 150 --mean_ratio 0 --recurrent --rnn_type LSTM --detach_gap 10 --stay_near_enemy --explore_vision 10 --step_size 16

• IC

python -u main.py --env_name starcraft --task_type explore --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 1 --our_unit_type 34 --enemy_unit_type 34 --init_range_end 150 --recurrent --rnn_type LSTM --detach_gap 10 --stay_near_enemy --explore_vision 10 --step_size 16

For 75x75, set --init_range_end to 175.

For Combat version:

• IC3Net

python -u main.py --env_name starcraft --task_type combat --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 3 --our_unit_type 0 --enemy_unit_type 65 --init_range_end 150 --ic3net --recurrent --rnn_type LSTM --detach_gap 10 --explore_vision 10 --step_size 16

• CommNet

python -u main.py --env_name starcraft --task_type combat --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 3 --our_unit_type 0 --enemy_unit_type 65 --init_range_end 150 --commnet --recurrent --rnn_type LSTM --detach_gap 10 --explore_vision 10 --step_size 16

• IRIC

python -u main.py --env_name starcraft --task_type combat --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 3 --our_unit_type 0 --enemy_unit_type 65 --init_range_end 150 --mean_ratio 0 --recurrent --rnn_type LSTM --detach_gap 10 --explore_vision 10 --step_size 16

• IC

python -u main.py --env_name starcraft --task_type combat --nagents 10 --num_epochs 1000 --hid_size 128 --lrate 0.002 --max_steps 60 --nprocesses 16 --torchcraft_dir=~/Public/TorchCraft --frame_skip 8 --nenemies 3 --our_unit_type 0 --enemy_unit_type 65 --init_range_end 150 --recurrent --rnn_type LSTM --detach_gap 10 --explore_vision 10 --step_size 16

Contributors

• Amanpreet Singh (@apsdehal)
• Tushar Jain (@tshrjn)
• Sainbayar Sukhbaatar (@tesatory)

License

Code is available under MIT license.