https://github.com/sweep76/bn-mappo-coordination
This is the official implementation of Multi-Agent PPO (MAPPO).
https://github.com/sweep76/bn-mappo-coordination
Last synced: 11 months ago
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This is the official implementation of Multi-Agent PPO (MAPPO).
- Host: GitHub
- URL: https://github.com/sweep76/bn-mappo-coordination
- Owner: Sweep76
- Created: 2023-11-14T00:25:19.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-28T11:11:49.000Z (almost 2 years ago)
- Last Synced: 2025-02-23T04:42:30.149Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 424 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# BNPG
## Training
1. Train fixed DAG topologies with tabular exact policy gradients on Coordination Game
```
cd Tabular_Coordination_Game
python run.py
```
2. Train context-aware DAG topologies with MAPPO on Coordination Game
```
cd BN_MAPPO_Coordination_Game/onpolicy/scripts/train
python train_coordination_game.py --graph_type [dummy, dynamic] --threshold [0,1]
```
3. Train context-aware DAG topologies with MAPPO on Aloha
```
cd BN_MAPPO_Aloha/onpolicy/scripts/train
python train_aloha.py --graph_type [dummy, dynamic] --threshold [0,1]
```
4. Train context-aware DAG topologies with MAPPO on SMAC
```
cd BN_MAPPO_SMAC/onpolicy/scripts/train
#on 6h_vs_8z, --use_annealing for annealing strategy
python train_aloha.py train_smac.py --use_annealing --graph_type [dummy, dynamic] --threshold [0,1] \
--alpha 0.1 --env_name StarCraft2 --algorithm_name mappo --use_recurrent_policy \
--map_name 6h_vs_8z --n_training_threads 1 --n_rollout_threads 8 --num_mini_batch 1 --episode_length 400 \
--num_env_steps 20000000 --ppo_epoch 5 --use_value_active_masks --use_eval --eval_episodes 32
#on MMM2, --use_annealing for annealing strategy
python train_aloha.py train_smac.py --use_annealing --graph_type [dummy, dynamic] --threshold [0,1] \
--alpha 0.05 --env_name StarCraft2 --algorithm_name mappo --use_recurrent_policy \
--map_name MMM2 --n_training_threads 1 --n_rollout_threads 8 --num_mini_batch 2 --episode_length 400 \
--num_env_steps 20000000 --ppo_epoch 5 --use_value_active_masks --use_eval --eval_episodes 32
```
### Acknowledgement
The MAPPO code is based on https://github.com/marlbenchmark/on-policy
The differentiable DAG sampling code is based on https://github.com/sharpenb/Differentiable-DAG-Sampling