🎮 A game-playing AI that can walk around and drive a car in a commercial game it has never trained on - and it learned to play without ever touching a real game, only synthetic environments. Title: Player-1: Learning to Play from Synthetic Experience URL: Here are 3 highlights. 🕹️ A game-specific action space Relative pointer motion handles camera control, "sustained inputs" let a key stay held down, and "composite actions" (like holding W while tapping jump) let the model express the kind of coordinated controls games actually need. 🏗️ RL training across 16 synthetic environments 16 verifiable 2D/3D environments cover aiming, combat, driving, navigation, and UI interaction, with appearance, camera, and control sensitivity all randomized together to avoid overfitting to any single game. 🚀 Generalizing to games it never trained on On Minecraft (MineDojo-300, outside the RL training set), success jumps from 9.21% to 38.33%; on ViZDoom, scaled return improves 83.7%. In the unseen commercial game Watch Dogs 2, it walks, approaches a vehicle, gets in, and drives through the city. A nice finding on top: scaling synthetic environments from 2 to 16 steadily improves generalization to unseen games, which is a practical takeaway for building future game agents. #GameAI# #ReinforcementLearning#