Options run python3 -m ay_game -helpfull. Types of players are supported (gamepad, external bots, agents.). The base scenario and the left player is controlled by the keyboard.

Q - switch the active player in the defense mode.

W - long pass in the attack mode, goalkeeper pressure in the defense mode.

D - shot in the attack mode, team pressure in the defense mode.

A - high pass in the attack mode, sliding in the defense mode.

S - short pass in the attack mode, pressure in the defense mode.

The game defines following keyboard mapping (for the keyboard player type): 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. Please note that playing the game is implemented through an environment, so human-controlled players use the same interface as the agents.

gfootball/examples/repro_scoring_easy.sh.

gfootball/examples/repro_checkpoint_easy.sh.

In order to reproduce PPO results from the paper, please refer to: Python3 -m _ppo2 -dump_full_episodes=True -render=True In order to train with nice replays being saved, run Python3 -m _ppo2 -level=academy_pass_and_shoot_with_keeper

To run on academy_pass_and_shoot_with_keeper scenario, run.

Python3 -m _ppo2 -level=academy_empty_goal_close Python3 -m pip install To run example PPO experiment on academy_empty_goal scenario, run

Sonnet and psutil: python3 -m pip install dm-sonnet=1.* psutil.

Python3 -m pip install tensorflow-gpu=1.15.*, depending on whether you want CPU or

TensorFlow: python3 -m pip install tensorflow=1.15.* or.

Update PIP, so that tensorflow 1.15 is available: python3 -m pip install -upgrade pip setuptools wheel.

In order to run TF training, you need to install additional dependencies To quit the game press Ctrl+C in the terminal. Make sure to check out the keyboard mappings. This is the recommended way for Linux-based systems to avoid incompatible package versions. This method doesn't support game rendering on screen - if you want to see the game running, please use the method below. Open our example Colab, that will allow you to start training your model in less than 2 minutes. We'd like to thank Bastiaan Konings Schuiling, who authored and open-sourced the original version of this game.

GRF Kaggle competition - take part in the competition playing games against others, win prizes and become the GRF Champion!.

Run in Colab - start training in less that 2 minutes.

It was created by the Google Brain team for research purposes. This repository contains an RL environment based on open-source game Gameplay

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