Google Research Football

Overview

Building AI agents that play 11v11 simulated football. Agents receive game observations (player positions, ball state, game mode) and return actions, competing head-to-head on Kaggle’s evaluation servers with Elo-style rating.

Approach

• Rule-based tactical foundation — “marauding wingers” formation: wide players sprint down flanks and deliver crosses into the box
• Zone-based decision architecture — field divided into zones (defensive third, wing corridors, crossing range, shooting range) with different behaviors per zone
• Opponent-aware mechanics — proximity detection for context-sensitive decisions: sprint in open space, dribble under pressure, pass when crowded
• Goalkeeper exploitation — specific logic to detect when the opposing keeper is out of position and trigger long-range shots
• Sprint/dribble state machine — manages action mode based on field position and opponent proximity

Result

61/1138 🥉

Links

• GitHub Repository

• Kaggle
• Reinforcement-Learning
• Agents