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import argparse
import json
import random
import sys
from pathlib import Path
from typing import Callable
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from cluster_trust_env import ClusterTrustEnv
from difficulty_controller import GLOBAL_DIFFICULTY_CONTROLLER
Policy = Callable[[ClusterTrustEnv, dict, random.Random], dict]
GROUND_TRUTH_RELIABILITY = {
"reliable": 0.93,
"slow": 0.78,
"degrading": 0.62,
"overreporter": 0.45,
"adversarial": 0.18,
}
def random_policy(env: ClusterTrustEnv, obs: dict, rng: random.Random) -> dict:
action_type = rng.choice(["allocate", "allocate", "request_info", "verify", "preempt", "tick"])
return _action_for_type(obs, action_type, rng, worker_id=rng.choice(obs["available_workers"]))
def blind_policy(env: ClusterTrustEnv, obs: dict, rng: random.Random) -> dict:
return _action_for_type(obs, "allocate", rng, worker_id=obs["available_workers"][0])
def trust_policy(env: ClusterTrustEnv, obs: dict, rng: random.Random) -> dict:
running = _jobs_with_status(obs, "running")
anomaly = max(obs["audit_anomaly_scores"].values() or [0.0])
if running and (obs["step_count"] % 4 == 0 or anomaly > 0.55):
return _action_for_type(obs, "verify", rng)
if _jobs_with_status(obs, "queued"):
return _action_for_type(obs, "allocate", rng, worker_id=_trust_calibrated_worker(obs))
if running:
return _action_for_type(obs, "request_info", rng)
return {"session_id": obs["session_id"], "action_type": "tick"}
def oracle_lite_policy(env: ClusterTrustEnv, obs: dict, rng: random.Random) -> dict:
running = _jobs_with_status(obs, "running")
state = env.state()
profile = state["worker_profile_hidden"]
best_worker = max(profile, key=lambda sid: GROUND_TRUTH_RELIABILITY[profile[sid]])
if running and state["attack_attempts"] > state["attack_detections"]:
return _action_for_type(obs, "verify", rng)
if _jobs_with_status(obs, "queued"):
return _action_for_type(obs, "allocate", rng, worker_id=best_worker)
if running:
return _action_for_type(obs, "request_info", rng, worker_id=best_worker)
return {"session_id": obs["session_id"], "action_type": "tick"}
def _action_for_type(obs: dict, action_type: str, rng: random.Random, worker_id: str | None = None) -> dict:
session_id = obs["session_id"]
running = _jobs_with_status(obs, "running")
pending = _jobs_with_status(obs, "queued")
if action_type == "allocate" and pending:
job = min(pending, key=lambda row: (row["deadline"], -row["memory_required"]))
gpu = max(obs["gpu_pool"], key=lambda row: row["memory_free"])
return {
"session_id": session_id,
"action_type": "allocate",
"job_id": job["job_id"],
"gpu_id": gpu["id"],
"worker_id": worker_id or _trust_calibrated_worker(obs),
}
if action_type in {"verify", "request_info", "preempt"} and running:
job = min(running, key=lambda row: row["deadline"])
payload = {"session_id": session_id, "action_type": action_type, "job_id": job["job_id"]}
if worker_id:
payload["worker_id"] = worker_id
return payload
return {"session_id": session_id, "action_type": "tick"}
def _jobs_with_status(obs: dict, status: str) -> list[dict]:
return [job for job in obs["job_queue_summary"] if job["status"] == status]
def _trust_calibrated_worker(obs: dict) -> str:
fingerprints = obs["behavioral_fingerprints"]
trust = obs["trust_snapshot"]
def score(worker_id: str) -> float:
calls = fingerprints[worker_id]["calls"]
gap = fingerprints[worker_id]["confidence_accuracy_gap"]
return trust[worker_id] + 0.25 / (1 + calls) - 0.20 * gap
min_calls = min(fingerprints[worker_id]["calls"] for worker_id in obs["available_workers"])
if min_calls < 2:
under_observed = [
worker_id for worker_id in obs["available_workers"]
if fingerprints[worker_id]["calls"] == min_calls
]
return max(under_observed, key=score)
return max(obs["available_workers"], key=score)
def run_episode(policy_name: str, policy: Policy, task_type: str, seed: int, adaptive: bool = False) -> dict:
rng = random.Random(seed)
env = ClusterTrustEnv()
result = env.reset(task_type=task_type, seed=seed, adaptive=adaptive)
rewards: list[float] = []
while not result["done"]:
action = policy(env, result["observation"], rng)
result = env.step(action)
rewards.append(result["reward"]["value"])
state = env.state()
coverage = state["ai_failure_coverage"]
attacks = state["attack_detections"] + state["attack_poisonings"]
detection_rate = state["attack_detections"] / max(1, attacks)
return {
"policy": policy_name,
"task_type": task_type,
"seed": seed,
"steps": state["step_count"],
"score": round(state["score"], 4),
"cluster_health": state["cluster"]["cluster_health_score"],
"utilization_rate": state["cluster"]["utilization_rate"],
"completion_rate": state["jobs"]["completion_rate"],
"deadline_hit_rate": state["jobs"]["deadline_hit_rate"],
"detection_rate": round(detection_rate, 4),
"attack_detections": state["attack_detections"],
"attack_poisonings": state["attack_poisonings"],
"ai_reliability_modifier": coverage["ai_reliability_modifier"],
"context_drift_events": coverage["context_memory_loss"]["drift_events"],
"loop_events": coverage["agent_loop_reliability"]["loop_events"],
"hallucination_confidence_score": coverage["hallucination_confidence"]["score"],
"evaluation_freshness_score": coverage["evaluation_collapse"]["score"],
"trust_snapshot": state["trust_snapshot"],
"difficulty_profile": state["difficulty_profile"],
"rewards": [round(value, 4) for value in rewards],
}
def summarize(rows: list[dict]) -> dict:
grouped: dict[str, list[dict]] = {}
for row in rows:
grouped.setdefault(row["policy"], []).append(row)
return {
policy: {
"episodes": len(items),
"avg_score": _avg(items, "score"),
"avg_cluster_health": _avg(items, "cluster_health"),
"avg_utilization_rate": _avg(items, "utilization_rate"),
"avg_completion_rate": _avg(items, "completion_rate"),
"avg_detection_rate": _avg(items, "detection_rate"),
"avg_ai_reliability_modifier": _avg(items, "ai_reliability_modifier"),
"avg_steps": _avg(items, "steps"),
}
for policy, items in sorted(grouped.items())
}
def _avg(rows: list[dict], key: str) -> float:
return round(sum(float(row.get(key, 0.0)) for row in rows) / max(1, len(rows)), 4)
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate SENTINEL GPU-cluster policies.")
parser.add_argument("--episodes", type=int, default=20)
parser.add_argument("--task", default="task3", choices=["task1", "task2", "task3", "all"])
parser.add_argument("--out", default="outputs/cluster_evaluation_results.json")
parser.add_argument("--adaptive", action="store_true")
parser.add_argument("--reset-difficulty", action="store_true")
args = parser.parse_args()
if args.reset_difficulty:
GLOBAL_DIFFICULTY_CONTROLLER.reset()
policies: dict[str, Policy] = {
"random": random_policy,
"blind": blind_policy,
"trust": trust_policy,
"oracle_lite": oracle_lite_policy,
}
tasks = ["task1", "task2", "task3"] if args.task == "all" else [args.task]
rows = [
run_episode(policy_name, policy, task_type, seed, adaptive=args.adaptive)
for task_type in tasks
for policy_name, policy in policies.items()
for seed in range(args.episodes)
]
payload = {
"environment": "cluster",
"tasks": tasks,
"episodes_per_policy": args.episodes,
"adaptive": args.adaptive,
"difficulty_controller": GLOBAL_DIFFICULTY_CONTROLLER.state(),
"summary": summarize(rows),
"episodes": rows,
}
out_path = ROOT / args.out
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(payload, indent=2) + "\n")
print(json.dumps({"summary": payload["summary"], "out": str(out_path.relative_to(ROOT))}, indent=2))
if __name__ == "__main__":
main()
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