Zeppelin Rush · Genetic AI

def fitness(game_result: dict) -> float:
    """
    game_result is parsed from SharedData.json after the game ends.
    Winning is rewarded by how much time was left (0..600 seconds).
    Losing is a large negative so the GA always prefers any win over
    any loss, but still gradients between 'lost fast' and 'lost slow'.
    """
    timer = game_result["Timer"]       # seconds left when game ended
    state = game_result["Gamestate"]   # "Win" or "Lose"
    if state == "Win":
        return timer                   # higher = finished faster
    return -600.0 + timer              # still a gradient between losses

def read_game_state(path="SharedData.json", max_retries=50):
    for _ in range(max_retries):
        try:
            with open(path, "r") as f:
                return json.load(f)["GameInfo"]
        except (IOError, json.JSONDecodeError):
            # Engine is mid-write. Back off one tick and retry.
            time.sleep(0.001)
    raise RuntimeError("SharedData.json never settled")

SELECT  = {"S", "M", "L"}
UPGRADE = {"H": "health", "A": "attack", "Q": "speed"}
SPAWNS  = ["Z", "X", "C"]   # top / middle / bottom lane

def FixMutation(actions):
    fixed = []
    selected = "S"                                   # small by default
    upgrades = {z: {"health": 0, "attack": 0, "speed": 0}
                for z in SELECT}

    for a in actions:
        if a in SELECT:
            # Illegal: two selects in a row. The previous select was
            # pointless, so replace THIS one with a random spawn.
            if fixed and fixed[-1] in SELECT:
                fixed.append(random.choice(SPAWNS)); continue
            selected = a
            fixed.append(a)
        elif a in UPGRADE:
            stat = UPGRADE[a]
            # Illegal: upgrading past the 2-upgrade cap.
            if upgrades[selected][stat] >= 2:
                fixed.append(random.choice(SPAWNS)); continue
            upgrades[selected][stat] += 1
            fixed.append(a)
        else:
            fixed.append(a)
    return fixed

ACTIONS = ["M", "L", "H", "A", "Q", "Z", "X", "C"]

def crossover(p1, p2):
    idx = random.randrange(1, min(len(p1), len(p2)))
    child1 = p1[:idx] + p2[idx:]
    child2 = p2[:idx] + p1[idx:]
    return FixMutation(child1), FixMutation(child2)

def mutate(genome, flips: int = 8):
    g = list(genome)
    for _ in range(flips):
        i = random.randrange(len(g))
        g[i] = random.choice(ACTIONS)
    return FixMutation(g)