Google Research Football
This repository contains an RL environment based on open-source game Gameplay
Football. It was created by the Google Brain team for research purposes.
Useful links:
- (NEW!) GRF Game Server - challenge other researchers!
- (NEW!) Run in Colab - start training in less that 2 minutes.
- Google Research Football Paper
- GoogleAI blog post
- Google Research Football on Cloud
- Mailing List - please use it for communication with us (comments / suggestions / feature ideas)
For non-public matters that you'd like to discuss directly with the GRF team,
please use google-research-football@google.com.
We'd like to thank Bastiaan Konings Schuiling, who authored and open-sourced the original version of this game.
Quick Start
In colab
Open our example Colab, that will allow you to start training your model in less than 2 minutes.
This method doesn't support game rendering on screen - if you want to see the game running, please use the method below.
On your computer
Install required apt packages with:
sudo apt-get install git cmake build-essential libgl1-mesa-dev libsdl2-dev \
libsdl2-image-dev libsdl2-ttf-dev libsdl2-gfx-dev libboost-all-dev \
libdirectfb-dev libst-dev mesa-utils xvfb x11vnc libsdl-sge-dev python3-pip
Then install the game from GitHub master:
git clone https://github.com/google-research/football.git
cd football
pip3 install .
This command can run for a couple of minutes, as it compiles the C++ environment in the background. Now, it's time to play!
python3 -m gfootball.play_game --action_set=full
Contents
- Running experiments
- Playing the game
- Environment API
- Observations
- Scenarios
- Multi-agent support
- Running in docker
- Saving replays, logs, traces
Training agents to play GRF
Run training
In order to run TF training, install additional dependencies:
- Update PIP, so that tensorflow 1.15rc2 is available:
python3 -m pip install --upgrade pip - TensorFlow:
pip3 install "tensorflow==1.15rc2"or
pip3 install "tensorflow-gpu==1.15rc2", depending on whether you want CPU or
GPU version; - Sonnet:
pip3 install dm-sonnet; - OpenAI Baselines:
pip3 install git+https://github.com/openai/baselines.git@master.
Then:
- To run example PPO experiment on
academy_empty_goalscenario, run
python3 -m gfootball.examples.run_ppo2 --level=academy_empty_goal_close - To run on
academy_pass_and_shoot_with_keeperscenario, run
python3 -m gfootball.examples.run_ppo2 --level=academy_pass_and_shoot_with_keeper
In order to train with nice replays being saved, run
python3 -m gfootball.examples.run_ppo2 --dump_full_episodes=True --render=True
In order to reproduce PPO results from the paper, please refer to:
- gfootball/examples/repro_checkpoint_easy.sh
- gfootball/examples/repro_scoring_easy.sh
Playing the game
Please note that playing the game is implemented through an environment, so human-controlled players use the same interface as the agents. One important implication is that there is a single action per 100 ms reported to the environment, which might cause a lag effect when playing.
Keyboard mappings
The game defines following keyboard mapping (for the keyboard player type):
ARROW UP- run to the top.ARROW DOWN- run to the bottom.ARROW LEFT- run to the left.ARROW RIGHT- run to the right.S- short pass in the attack mode, pressure in the defense mode.A- high pass in the attack mode, sliding in the defense mode.D- shot in the the attack mode, team pressure in the defense mode.W- long pass in the the attack mode, goalkeeper pressure in the defense mode.Q- switch the active player in the defense mode.C- dribble in the attack mode.E- sprint.
Play vs built-in AI
Run python3 -m gfootball.play_game --action_set=full. By default, it starts
the base scenario and the left player is controlled by the keyboard. Different
types of players are supported (gamepad, external bots, agents...). For possible
options run python3 -m gfootball.play_game -helpfull.
Play vs pre-trained agent
In particular, one can play against agent trained with run_ppo2 script with
the following command (notice no action_set flag, as PPO agent uses default
action set):
python3 -m gfootball.play_game --players "keyboard:left_players=1;ppo2_cnn:right_players=1,checkpoint=$YOUR_PATH"
Trained checkpoints
We provide trained PPO checkpoints for the following scenarios:
In order to see the checkpoints playing, run
python3 -m gfootball.play_game --players "ppo2_cnn:left_players=1,policy=gfootball_impala_cnn,checkpoint=$CHECKPOINT" --level=$LEVEL,
where $CHECKPOINT is the path to downloaded checkpoint.
In order to train against a checkpoint, you can pass 'extra_players' argument to create_environment function.
For example extra_players='ppo2_cnn:right_players=1,policy=gfootball_impala_cnn,checkpoint=$CHECKPOINT'.
Frequent Problems & Solutions
Rendering not working / "OpenGL version not equal to or higher than 3.2"
Solution: set environment variables for MESA driver, like this:
MESA_GL_VERSION_OVERRIDE=3.2 MESA_GLSL_VERSION_OVERRIDE=150 python3 -m gfootball.play_game