Installation and Usage Guide:

In this repository we provide details of our interace, installation guidelines, links to saved checkpoints and training logs, and instructions to reproduce the experiments.

Installation

1. Set up Python3.7 and Poetry

1. Install Python 3.7
2. Install Poetry. Please refer to: https://python-poetry.org/docs/#installation

2. Install Google Research Football and Our Interface

3. Download the codebase we shared.
4. Create a virtual environment in ScenicGFootBall using poetry env use python3.7. Activate it using poetry shell.
5. In ScenicGFootBall run poetry install This will install Scenic and Our Interface in editable mode.
6. Install Google Research Football. Please refer to: https://github.com/google-research/football#on-your-computer
7. Install RL Training dependencies including Tensorflow 1.15, Sonnet, and OpenAI Baselines. Please refer to: https://github.com/google-research/football#run-training
8. Go to the folder training and run python3 -m pip install -e . to install our package gfrl which we use to conduct the experiments.

3. Test the installation by running:

Run the following program to create an environment with a scenic scenario script and running a random agent.

from scenic.simulators.gfootball.utilities.scenic_helper import buildScenario
scenario = buildScenario("..path to a scenic script..") #Find all our scenarios in training/gfrl/_scenarios/

from scenic.simulators.gfootball.rl.gfScenicEnv_v2 import GFScenicEnv_v2

env_settings = {
    "stacked": True,
    "rewards": 'scoring',
    "representation": 'extracted',
    "players": [f"agent:left_players=1"],
    "real_time": True
    }
env = GFScenicEnv_v2(initial_scenario=scenario, gf_env_settings=env_settings)

env.reset()
done = False

while not done:
    action = env.action_space.sample()
    _, _, done, _ = env.step(action)

Libraries and dataset of scenarios and probabilistic scenic policies

Libraries

Our model, action, and behavior libraries can be found in the directory src/scenic/simulators/gfootball/ in model.scenic, actions.py , and behaviors.scenic respectively.

Scenarios

All of our proposed scenarios can be found in the training/gfrl/_scenarios directory, categorized according to their type. Proposed Defense and Offense Scenarios are placed in training/gfrl/_scenarios/defense and training/gfrl/_scenarios/offense directories, respectively. Scenic scenario scripts corresponding to select GRF scenarios can be found in training/gfrl/_scenarios/grf. training/gfrl/_scenarios/testing_generalization contains testing scripts corresponding to all the above mentioned scenarios.

Scenic Policies and Demonstration Data

Scenic semi-expert policy scripts for select scenarios can be found in training/gfrl/_scenarios/demonstration. Data generated from these policies are placed in training/gfrl/_demonstration_data.

Interface

Create Environment

First Create Scenic scenario object:

from scenic.simulators.gfootball.utilities.scenic_helper import buildScenario  
scenario = buildScenario(scenario_file)

Then, Create Gym Environment from Scenic scenario:

env_type : We now offer two different environment classes, i.e., GFScenicEnv_v1 and GFScenicEnv_v2. We recommend to use GFScenicEnv_v2, which is our default environment implementing all the features and experiments discussed in the paper. GFScenicEnv_v1 only allows the initial distribution of states, but doesnt allow to use Scenic behaviors for non-RL agents, i.e., it always uses the Default AI behavior provided by GRF for the non-RL agents.

from scenic.simulators.gfootball.rl.gfScenicEnv_v1 import GFScenicEnv_v1  
from scenic.simulators.gfootball.rl.gfScenicEnv_v2 import GFScenicEnv_v2  
 
env_type = "... use appropriate env class according to your need..."
if env_type=="v1":  
   env = GFScenicEnv_v1(initial_scenario=scenario, gf_env_settings=gf_env_settings, compute_scenic_behavior=True)  
elif env_type=="v2":  
   env = GFScenicEnv_v2(initial_scenario=scenario, gf_env_settings=gf_env_settings)  
else:  
   assert False, "invalid env_type"

Please refer to training/gfrl/base/bc/utils.py for further details.

Generate Demonstration Data from Scenic Scenario Scripts

For generating expert data using Scenic policies, we need to execute scenic policies at each timestep to generate an action. At any timestep, one can use the environment method env.simulation.get_scenic_designated_player_action() to read the action that was computed corresponding to the active player.

We provide helper scripts to automate the process though. One can simply follow the following steps to generate data:

1. Open training/gfrl/experiments/gen_demonstration.py

2. Change the following fields:

   • scenario: Path to the scenic policy scripts
   • data_path: The output file path without any extension.

3. Run python3 training/gfrl/experiments/gen_demonstration.py

To read the saved offline data, run the following:

from gfrl.common.mybase.cloning.dataset import get_datasets
tds, vds = get_datasets("..path to the saved data...", validation_ratio=0.0)

print("train")
print(tds.summary())
print()

print("validation")
print(vds.summary())
print()

Reproducability

Run training with PPO

In order to reproduce PPO results from the paper, please refer to:

• training/gfrl/experiments/score_scenic.sh

Train agents from Demonstration Data using Behavior Cloning

1. Open training/gfrl/experiments/bc.sh
2. Change the following fields:
   • level: Path to the scenic scenario scripts
   • eval_level: Should be the same as level
   • dataset: The .npz demonstration data file generated by gen_demonstration.py.
   • n_epochs: Change it to 5 for offense and 16 for grf scenarios to reproduce the paper's results
   • exp_root: where to store the training outputs
   • exp_name: output directory's name
3. Run bash training/gfrl/experiments/bc.sh

Train the pretrained-agents (e.g., from behavior cloning) with PPO

With default settings, the script will train the model for 5M timesteps.

1. Open training/gfrl/experiments/pretrain.sh
2. Change the following fields:
   • level: Path to the Scenic scenario scripts
   • eval_level: Should be the same as level
   • exp_root: where to store the training outputs
   • exp_name: output directory's name
   • load_path: path to the behavior cloning model
3. Run bash training/gfrl/experiments/pretrain.sh

Testing for Generalization

Please use the following script to evaluate the model's mean score.

1. Open training/gfrl/experiments/test_agent.sh
2. Change the following fields:
   • eval_level: path to the Scenic scenario scripts
   • load_path: path to the model to be evaluated
   • write_video: Set it to False
   • dump_full_episodes: Set it to False
3. Run bash training/gfrl/experiments/test_agent.sh
4. The result will be printed in the end in the following format: exp_name, reward_mean, score_mean, ep_len_mean, num_test_epi, test_total_timesteps, eval_level, load_path.

Trained checkpoints

We pubicly share tensorboard log and saved checkpoints for all our experiments here.

Acknowledgement

We'd like to thank the Scenic and GRF Team for open sourcing their projects.