Building a Game Boy Emulator for AI Training

Building a Game Boy Emulator for AI Training What is rlgb? rlgb is a fast Game Boy emulator written in C from scratch. But it's not built for playing games - it's built for training AI agents to play games like Pokemon Red. Why Build an Emulator? Most emulators are designed to be accurate and support every game ever made. They're slow (100-500 fps). For AI training, you need: - SPEED: Process thousands of game frames per second - DETERMINISM: Same input = same output every time (for reproducibility) - HEADLESS: No graphics - just the game logic rlgb does all three. Performance Numbers - Speed: ~3,200 frames per second (single core) - Save state size: ~167 KB (very small, can save quickly) - Deterministic: Yes - every frame is reproducible - Supported games: Pokemon Red/Blue, most MBC1/3/5 cartridges How It Works The emulator simulates the Game Boy hardware: 1. CPU (SM83): Execute game instructions one by one 2. Graphics (PPU): Calculate what's displayed on screen (but we don't render it) 3. Memory: Store the game state (~167 KB) 4. Cartridge: Read game data from disk Everything runs per-instruction, not per-cycle, so it's fast enough for AI. Using It With AI Training The real magic is using rlgb with Stable Baselines 3 (an AI framework): ```python from rlgb_vec import make_gb_vec_env, build_config # Create 12 game instances running in parallel config = build_config("pokemon-red.gb", n_envs=12) vec_env = make_gb_vec_env(config) # Train AI agent with PPO algorithm from stable_baselines3 import PPO model = PPO("CnnPolicy", vec_env) model.learn(total_timesteps=1_000_000) ``` What Can AI Learn? - Navigate the game world - Defeat gym leaders in the right order - Manage Pokemon team (leveling, type matchups) - Explore efficiently - Beat the game autonomously This Shows - AI can learn complex, long-term goals - Game emulation is perfect for testing AI - You can build AI training systems yourself - Speed matters: 3,200 fps = fast iteration