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!
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.
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
In order to run TF training, install additional dependencies:
TensorFlow: pip3 install "tensorflow<2.0"
orpip3 install "tensorflow-gpu<2.0"
, 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_goal
scenario, runpython3 -m gfootball.examples.run_ppo2 --level=academy_empty_goal_close
To run on academy_pass_and_shoot_with_keeper
scenario, runpython3 -m gfootball.examples.run_ppo2 --level=academy_pass_and_shoot_with_keeper
In order to train with nice replays being saved, runpython3 -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
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.
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.
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
.
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=$YOUR_PATH"
We provide trained PPO checkpoints for the following scenarios:
In order to see the checkpoints playing, runpython3 -m gfootball.play_game --players
"ppo2_cnn:left_players=1,policy=gfootball_impala_cnn,checkpoint=$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'.
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
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