IC3Net

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

★ 232 Open GitHub ↗

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

다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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](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](https://github.com/apsdehal/gym-starcraft)
- Find `ic3net-envs` at this repository: [apsdehal/ic3net-envs](https://github.com/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](https://github.com/apsdehal))
- Tushar Jain ([@tshrjn](https://github.com/tshrjn))
- Sainbayar Sukhbaatar ([@tesatory](https://github.com/tesatory))

## License

Code is available under MIT license.

IC3Net

다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

IC3Net

다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README