114 lines
5.0 KiB
Python
114 lines
5.0 KiB
Python
"""问题3/4 RL 策略与既有策略的配对自建消融。
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同一种子、同一场景分别跑基线（Q3 bayes / Q4 directional）与 RL 变体，
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比较全部清除成功、虚拟总时间、检测次数与请求总数。结果写入
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output/rl/benchmark.json。
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用法：
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uv sync --group rl --group dev
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uv run python scripts/benchmark_rl.py --seeds 5
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"""
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from __future__ import annotations
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import argparse
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import json
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import statistics
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import time
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from pathlib import Path
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from mcm26b.config import ProblemConfig
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from mcm26b.harness import (LocalSimTransport, mixed_scene, random_omni_scene,
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referee_spec)
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from mcm26b.planner import DirectionalExtension
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from mcm26b.planner.bayes_loop import BayesLoop
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from mcm26b.planner.rl_loop import RLBayesLoop, RLDirectionalLoop
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from mcm26b.protocol.client import SimulatorClient
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from referee import LocalSimulator
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def run_one(loop_cls, sources, *, config, robot_id: str, **kwargs) -> dict:
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simulator = LocalSimulator(sources, robot_id=robot_id,
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spec=referee_spec(config, max_real_duration_s=3600.0),
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stress_mode=True)
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client = SimulatorClient(robot_id, config=config,
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transport=LocalSimTransport(simulator))
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started = time.monotonic()
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summary = loop_cls(client, config, **kwargs).run() \
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if kwargs else loop_cls(client, config).run()
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return {
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"completed": bool(summary["completed"]),
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"virtual_time_s": float(summary["virtual_time_s"]),
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"measures": int(summary["measures"]),
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"total_requests": int(summary["total_requests"]),
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"move_distance_m": float(summary["move_distance_m"]),
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"wall_s": round(time.monotonic() - started, 3),
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}
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def main(argv=None) -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--seeds", type=int, default=5)
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parser.add_argument("--sources", type=int, default=10)
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parser.add_argument("--directional", type=int, default=3)
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parser.add_argument("--output", default="output/rl/benchmark.json")
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args = parser.parse_args(argv)
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config = ProblemConfig()
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robot_id = "2026001"
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report = {"config": {"seeds": args.seeds, "sources": args.sources,
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"directional": args.directional},
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"q3": [], "q4": []}
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for seed in range(20260910, 20260910 + args.seeds):
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omni = random_omni_scene(seed=seed, count=args.sources, config=config)
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mixed = mixed_scene(seed=seed, count=args.sources,
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directional=args.directional, config=config)
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q3_base = run_one(BayesLoop, omni, config=config, robot_id=robot_id)
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q3_rl = run_one(RLBayesLoop, omni, config=config, robot_id=robot_id)
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q4_base = run_one(DirectionalExtension, mixed, config=config,
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robot_id=robot_id)
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q4_rl = run_one(RLDirectionalLoop, mixed, config=config,
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robot_id=robot_id)
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report["q3"].append({"seed": seed, "bayes": q3_base, "rl": q3_rl})
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report["q4"].append({"seed": seed, "directional": q4_base, "rl": q4_rl})
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print(json.dumps({"event": "seed_done", "seed": seed,
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"q3_base_s": q3_base["virtual_time_s"],
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"q3_rl_s": q3_rl["virtual_time_s"],
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"q4_base_s": q4_base["virtual_time_s"],
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"q4_rl_s": q4_rl["virtual_time_s"]},
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ensure_ascii=False))
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for key, baseline in (("q3", "bayes"), ("q4", "directional")):
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rows = report[key]
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deltas = [row["rl"]["virtual_time_s"] - row[baseline]["virtual_time_s"]
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for row in rows]
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cleared = all(row["rl"]["completed"] and row[baseline]["completed"]
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for row in rows)
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report[key + "_summary"] = {
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"mean_virtual_s": {
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baseline: statistics.mean(row[baseline]["virtual_time_s"] for row in rows),
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"rl": statistics.mean(row["rl"]["virtual_time_s"] for row in rows),
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},
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"mean_delta_s": statistics.mean(deltas),
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"relative_delta_pct": 100.0 * statistics.mean(deltas)
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/ statistics.mean(row[baseline]["virtual_time_s"] for row in rows),
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"mean_measures": {
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baseline: statistics.mean(row[baseline]["measures"] for row in rows),
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"rl": statistics.mean(row["rl"]["measures"] for row in rows),
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},
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"all_cleared": cleared,
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}
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output = Path(args.output)
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output.parent.mkdir(parents=True, exist_ok=True)
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output.write_text(json.dumps(report, ensure_ascii=False, indent=2),
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encoding="utf-8")
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print(json.dumps({"event": "benchmark_done", "path": str(output),
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"q3": report["q3_summary"],
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"q4": report["q4_summary"]},
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ensure_ascii=False, indent=2))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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