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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 🥉