https://galliard7.github.io/tags/reinforcement-learning/Recent content in Reinforcement-Learning on Galliard7Hugoen-usTue, 15 Sep 2020 00:00:00 +0000https://galliard7.github.io/projects/google-research-football/Tue, 15 Sep 2020 00:00:00 +0000https://galliard7.github.io/projects/google-research-football/<h2 id="overview">Overview</h2> <p>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&rsquo;s evaluation servers with Elo-style rating.</p> <h2 id="approach">Approach</h2> <ul> <li><strong>Rule-based tactical foundation</strong> — &ldquo;marauding wingers&rdquo; formation: wide players sprint down flanks and deliver crosses into the box</li> <li><strong>Zone-based decision architecture</strong> — field divided into zones (defensive third, wing corridors, crossing range, shooting range) with different behaviors per zone</li> <li><strong>Opponent-aware mechanics</strong> — proximity detection for context-sensitive decisions: sprint in open space, dribble under pressure, pass when crowded</li> <li><strong>Goalkeeper exploitation</strong> — specific logic to detect when the opposing keeper is out of position and trigger long-range shots</li> <li><strong>Sprint/dribble state machine</strong> — manages action mode based on field position and opponent proximity</li> </ul> <h2 id="result">Result</h2> <p>61/1138 🥉</p>