File size: 3,499 Bytes
324ecd2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | """Baselines on held-out TEST levels (seeds 1_000_000+) for SUPERBEAM; CPU only, emulator backend.
.venv/bin/python scripts/gym_baselines_levels.py [--levels 20] [--difficulty 0.5]
Agents: random; scripted (hold right, jump when the frame shows a pit, a pipe/tall pipe (3 lit
pixels in a row at the level it must clear) or a walker within a lead; perception = the 64x32 frame
plus player x/y only); scripted+eps0.1. Writes results/gym_baselines_levels.json.
"""
import argparse
import json
import os
import sys
import numpy as np
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, ROOT)
sys.path.insert(0, os.path.join(ROOT, "scripts"))
from beamboot.superbeam_gym import GROUND_Y, PIPE_Y, SuperBeamEnv # noqa: E402
from beamboot.superbeam_levels import TEST_SEEDS # noqa: E402
from gym_baselines import RandomAgent, run_episode, obstacle_ahead # noqa: E402
OUT = os.path.join(ROOT, "results", "gym_baselines_levels.json")
def wall_ahead(obs, lead):
f, x, y = obs["frame"], obs["x"], obs["y"]
row = 16 if y == GROUND_Y else 8
for d in range(0, lead + 1):
c = x + 8 + d
if c + 2 < 64 and f[row, c] and f[row, c + 1] and f[row, c + 2]:
return d
return None
class Scripted:
def __init__(self, pit_lead=4, enemy_lead=14, wall_lead=4, eps=0.0, seed=0):
self.pl, self.el, self.wl, self.eps = pit_lead, enemy_lead, wall_lead, eps
self.rng = np.random.default_rng(seed)
def act(self, obs):
if self.eps and self.rng.random() < self.eps:
return int(self.rng.integers(6))
if obs["y"] not in (GROUND_Y, PIPE_Y, 2):
return 2
pit, enemy = obstacle_ahead(obs, max(self.pl, self.el)) if obs["y"] == GROUND_Y else (None, None)
wall = wall_ahead(obs, self.wl)
if (pit is not None and pit <= self.pl) or (enemy is not None and enemy <= self.el) or wall is not None:
return 4
return 2
def evaluate(name, make_agent, seeds, args):
eps = []
for s in seeds:
env = SuperBeamEnv(level=s, difficulty=args.difficulty, frame_skip=4, max_frames=600)
r = run_episode(env, make_agent(s))
r["coins"] = env.n_coins
r["seed"] = s
eps.append(r)
return {"agent": name, "episodes": len(eps),
"levels_won": sum(e["won"] for e in eps),
"win_rate": sum(e["won"] for e in eps) / len(eps),
"mean_progress": float(np.mean([e["progress"] for e in eps])),
"mean_coins": float(np.mean([e["coins"] for e in eps])),
"episodes_detail": eps}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--levels", type=int, default=20)
ap.add_argument("--difficulty", type=float, default=0.5)
args = ap.parse_args()
seeds = [TEST_SEEDS.start + i for i in range(args.levels)]
rows = [evaluate("random", lambda s: RandomAgent(s), seeds, args),
evaluate("scripted", lambda s: Scripted(), seeds, args),
evaluate("scripted+eps0.1", lambda s: Scripted(eps=0.1, seed=s), seeds, args)]
for r in rows:
print("%-16s won %d/%d progress %.3f coins %.2f" % (r["agent"], r["levels_won"], r["episodes"],
r["mean_progress"], r["mean_coins"]))
with open(OUT, "w") as f:
json.dump({"levels": seeds, "difficulty": args.difficulty, "frame_skip": 4, "results": rows}, f, indent=1)
if __name__ == "__main__":
main()
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