scalableMARL
    |___algos
        |___maTT                          #RL alg folder for the target tracking environment
            |___core                      #Self-Attention-based Model Architecture
            |___core_behavior             #Used for further evaluation (Ablation D.2.)
            |___dql                       #Soft Double Q-Learning
            |___evaluation                #Evaluation for Main Results
            |___evaluation_behavior       #Used for further evaluation (Ablation D.2.)
            |___modules                   #Self-Attention blocks
            |___replay_buffer             #RL replay buffer for sets
            |___run_script                #**Main run script to do training and evaluation
    |___envs
        |___maTTenv                       #multi-agent target tracking
            |___env
                |___setTracking_v0        #Standard environment (i.e. 4a4t tasks)
                |___setTracking_vGreedy   #Baseline Greedy Heuristic
                |___setTracking_vGru      #Experiment with Gru (Ablation D.3)
                |___setTracking_vkGreedy  #Experiment with Scalability and Heuristic Mask k=4 (Ablation D.1)
        |___run_ma_tracking               #Example scipt to run environment
    |___setup                             #set PYTHONPATH ($source setup)

#to check python version
python3 -V

sudo apt-get update
sudo apt-get install python3.8-dev

conda env create -f environment.yml
conda activate scalableMARL
source setup

sudo apt-get install python3-venv 

python3 -m venv --system-site-packages ./'virtualenv name'
# Activate the environment for use, any commands now will be done within this venv
source ./'virtualenv name'/bin/activate

# To deactivate (in terminal, exit out of venv), do not use during setup
deactivate

sudo apt-get install -y eog python3-tk python3-yaml python3-pip ssh git

#This command will auto install packages from requirements.txt
pip3 install --trusted-host pypi.python.org -r requirements.txt

# activate env
conda activate scalableMARL
# or
source ./'virtualenv name'/bin/activate
# change directory to scalableMARL
cd ./scalableMARL
# setup repo  ***important in order to set PYTHONPATH***
source setup

# its best to run from the scalableMARL folder so that logging and saving is consistent
cd ./scalableMARL
# run the alg
python3 algos/maTT/run_script.py

# you can run the alg with different argument parameters. See within run_script for more options.
# for example
python3 algos/maTT/run_script.py --seed 0 --logdir ./results/maPredPrey --epochs 40

# for a general example 
python3 algos/maTT/run_script.py --mode test --render 1 --log_dir ./results/maTT/setTracking-v0_123456789/seed_0/ --nb_test_eps 50
# for a saved policy in saved_results
python3 algos/maTT/run_script.py --mode test --render 1 --log_dir ./saved_results/maTT/setTracking-v0_123456789/seed_0/

tensorboard --logdir ./results/maTT/setTracking-v0_123456789/

@misc{hsu2021scalable,
      title={Scalable Reinforcement Learning Policies for Multi-Agent Control}, 
      author={Christopher D. Hsu and Heejin Jeong and George J. Pappas and Pratik Chaudhari},
      year={2021},
      eprint={2011.08055},
      archivePrefix={arXiv},
      primaryClass={cs.MA}
}