Structure of rl_algos/
├── ddqn.py ├── ddqn_modified.py ├── deep_sarsa.py ├── dqn.py ├── ddpg.py ├── ma-sarsa.py ├── maddpg.py ├── ppo.py ├── q_learning.py └── sarsa.py Structure of multi_uav_coverage_maddpg/
├── env.py ├── test.py ├── train.py ├── utils/ │ ├── data_points.py │ ├── decoded_points.py │ ├── input.py │ ├── logger.py │ └── plot_logs.py └── maddpg/ ├── agents.py ├── buffer.py ├── cnn.py └── maddpg_uav.py - Clone the repository :
git@github.com:Project-Group-LBP/LBP.git. - Create a virtual environment and activate it.
- Install requirements using
pip install -r requirements.txt.
To train the MADDPG model:
cd multi_uav_coverage_maddpg # Basic training with default settings (500 episodes) python train.py # Train with custom number of episodes python train.py --num_episodes=1000 # Train using image initialization python train.py --use_img --img_path="path/to/image.png" # Resume training from saved model python train.py --resume="saved_models/maddpg_episode_100" # can input pending no of episodesTo test a trained model:
cd multi_uav_coverage_maddpg # Basic testing with default settings (50 episodes) python test.py --model_path="saved_models/maddpg_episode_final" # Test with custom number of episodes python test.py --model_path="saved_models/maddpg_episode_final" --num_episodes=25 # Test with image initialization python test.py --model_path="saved_models/maddpg_episode_final" --use_img --img_path="path/to/image.png"