Jowa Football AI v2

What's New in v2

Upgraded from 3v3 to 6v6 with Formation 3-2-1

AI replaced from Q-table to DQN (Deep Q-Network) using PyTorch

Shared replay buffer with capacity 200,000 transitions

Player selection system — pick any player with ↑↓ then Enter

Auto epsilon decay with periodic model saving every 3600 frames

CUDA support — runs on GPU if available

Mode	6v6 (Red vs Blue)
Formation	3-2-1
AI Type	DQN — Deep Q-Network (PyTorch)
Actions	9 — Stop, N, S, E, W, NE, NW, SE, SW
Resolution	800 × 600
FPS	60
Device	CUDA (GPU) or CPU — auto-detected
Model Files	football_model_red.pth, football_model_blue.pth

Python 3.8+

Required to run the script

pygame

pip install pygame

PyTorch

pip install torch

NumPy

pip install numpy

1

pip install pygame torch numpy

2

Extract jowa-football-ai.zip to any folder.

3

python jowa-football-ai-with-players_PLAYMODEAI4_6p.py

↑ / ↓ — cycle through players | Enter / Space — control selected player | ESC — back to selection

W Up | S Down | A Left | D Right

↑ Up | ↓ Down | ← Left | → Right