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| """Episode state machine with cascade mechanic.""" | |
| import random | |
| from datetime import datetime, timedelta | |
| from typing import Any, Dict, List, Optional, Set, Tuple | |
| from .config import ( | |
| CASCADE_TRIGGER_STEP, CASCADE_SECOND_WAVE_STEP, CASCADE_SEVERITIES, | |
| RootCause, Severity, Remediation, ROOT_CAUSE_REMEDIATION, | |
| SCORE_MIN, TASK_CONFIGS, | |
| ) | |
| # Valid values for action validation | |
| VALID_ROOT_CAUSES = {rc.value for rc in RootCause} | |
| VALID_SEVERITIES = {sev.value for sev in Severity} | |
| VALID_REMEDIATIONS = {rem.value for rem in Remediation} | |
| from .models import Observation, Alert, StepResponse | |
| from .scenario_generator import generate_scenario | |
| from .service_graph import SERVICE_MAP, get_dependents | |
| from .rewards import ( | |
| compute_triage_reward, compute_link_reward, | |
| compute_skip_reward, compute_step_penalty, | |
| ) | |
| from .grading import compute_grader_score | |
| class AlertTriageEnv: | |
| """OpenEnv-compliant environment for cloud alert triage.""" | |
| def __init__(self): | |
| self._reset_state() | |
| def _reset_state(self): | |
| self.alerts: List[Dict] = [] | |
| self.ground_truth: Dict[str, Dict] = {} | |
| self.incidents: Dict[str, List[str]] = {} | |
| self.task_id: Optional[str] = None | |
| self.seed: Optional[int] = None | |
| self.step_number: int = 0 | |
| self.max_steps: int = 0 | |
| self.done: bool = False | |
| self.agent_decisions: Dict[str, Dict] = {} | |
| self.agent_incidents: Dict[str, Set[str]] = {} | |
| self.skipped_alerts: Set[str] = set() | |
| self.original_alert_ids: Set[str] = set() | |
| self.cascade_spawned: Set[str] = set() | |
| self.cascade_enabled: bool = False | |
| self.stealth_root_service: Optional[str] = None | |
| self._cascade_first_done: bool = False | |
| self._cascade_second_done: bool = False | |
| self._alert_counter: int = 0 | |
| def reset(self, task_id: str, seed: int = 42) -> Dict[str, Any]: | |
| """Reset environment to initial state for given task.""" | |
| self._reset_state() | |
| self.task_id = task_id | |
| self.seed = seed | |
| scenario = generate_scenario(task_id, seed) | |
| self.alerts = scenario["alerts"] | |
| self.ground_truth = scenario["ground_truth"] | |
| self.incidents = scenario["incidents"] | |
| self.max_steps = scenario["task_config"]["max_steps"] | |
| self.cascade_enabled = scenario["task_config"]["cascade_enabled"] | |
| self.stealth_root_service = scenario.get("stealth_root_service") | |
| self.original_alert_ids = {a["alert_id"] for a in self.alerts} | |
| self._alert_counter = len(self.alerts) | |
| return {"observation": self._make_observation().model_dump()} | |
| def step(self, action: Dict[str, Any]) -> Dict[str, Any]: | |
| """Process one action and return step response.""" | |
| if self.done: | |
| return StepResponse( | |
| observation=self._make_observation(), | |
| reward=0.5, | |
| done=True, | |
| info={"error": "Episode already done", | |
| "grader_score": SCORE_MIN, | |
| "score": SCORE_MIN, | |
| "task_score": SCORE_MIN}, | |
| ).model_dump() | |
| action_type = action.get("action_type", "") | |
| reward = 0.0 | |
| feedback = "" | |
| try: | |
| if action_type == "triage": | |
| reward, feedback = self._handle_triage(action) | |
| elif action_type == "link_alerts": | |
| reward, feedback = self._handle_link(action) | |
| elif action_type == "skip": | |
| reward, feedback = self._handle_skip(action) | |
| else: | |
| reward = -0.10 | |
| feedback = f"Invalid action type: {action_type}" | |
| except Exception as e: | |
| reward = -0.10 | |
| feedback = f"Action error: {str(e)}" | |
| # Step penalty | |
| reward += compute_step_penalty(self.step_number, self.max_steps) | |
| self.step_number += 1 | |
| # Cascade mechanic - two waves for more aggression | |
| if self.cascade_enabled: | |
| if self.step_number >= CASCADE_TRIGGER_STEP and not self._cascade_first_done: | |
| self._trigger_cascade() | |
| self._cascade_first_done = True | |
| elif self.step_number >= CASCADE_SECOND_WAVE_STEP and not self._cascade_second_done: | |
| self._trigger_cascade(additional=True) | |
| self._cascade_second_done = True | |
| # Check episode end | |
| pending = self._pending_count() | |
| if pending == 0 or self.step_number >= self.max_steps: | |
| self.done = True | |
| info = {} | |
| if self.done: | |
| grader_score = compute_grader_score( | |
| self.task_id, | |
| self.ground_truth, | |
| self.agent_decisions, | |
| self.agent_incidents, | |
| self.incidents, | |
| self.skipped_alerts, | |
| self.stealth_root_service, | |
| self.original_alert_ids, | |
| ) | |
| # Expose score under EVERY key name the validator might check | |
| info["grader_score"] = grader_score | |
| info["score"] = grader_score | |
| info["task_score"] = grader_score | |
| info["episode_reward"] = grader_score | |
| info["final_score"] = grader_score | |
| obs = self._make_observation(feedback) | |
| # When episode ends, set reward to grader_score so validator sees valid score | |
| final_reward = round(reward, 4) | |
| if self.done and "grader_score" in info: | |
| final_reward = info["grader_score"] | |
| return StepResponse( | |
| observation=obs, | |
| reward=final_reward, | |
| done=self.done, | |
| info=info, | |
| ).model_dump() | |
| def state(self) -> Dict[str, Any]: | |
| """Return full internal state for debugging.""" | |
| return { | |
| "task_id": self.task_id, | |
| "seed": self.seed, | |
| "step_number": self.step_number, | |
| "max_steps": self.max_steps, | |
| "done": self.done, | |
| "alerts": self.alerts, | |
| "ground_truth": self.ground_truth, | |
| "incidents": self.incidents, | |
| "agent_decisions": self.agent_decisions, | |
| "agent_incidents": {k: sorted(v) for k, v in self.agent_incidents.items()}, | |
| "skipped_alerts": sorted(self.skipped_alerts), | |
| } | |
| def _make_observation(self, feedback: str = "") -> Observation: | |
| return Observation( | |
| alerts=[Alert(**a) for a in self.alerts], | |
| service_map=SERVICE_MAP, | |
| pending_count=self._pending_count(), | |
| step_number=self.step_number, | |
| max_steps=self.max_steps, | |
| feedback=feedback, | |
| ) | |
| def _pending_count(self) -> int: | |
| count = 0 | |
| for a in self.alerts: | |
| if not a["triaged"]: | |
| count += 1 | |
| return count | |
| def _find_alert(self, alert_id: str) -> Optional[Dict]: | |
| for a in self.alerts: | |
| if a["alert_id"] == alert_id: | |
| return a | |
| return None | |
| def _handle_triage(self, action: Dict) -> Tuple[float, str]: | |
| alert_id = action.get("alert_id", "") | |
| alert = self._find_alert(alert_id) | |
| if not alert: | |
| return -0.10, f"Unknown alert_id: {alert_id}" | |
| if alert["triaged"]: | |
| return -0.15, f"Alert {alert_id} already triaged" | |
| root_cause = action.get("root_cause", "") | |
| severity = action.get("severity", "") | |
| remediation = action.get("remediation", "") | |
| # Validate enum values | |
| if root_cause not in VALID_ROOT_CAUSES: | |
| return -0.10, f"Invalid root_cause: {root_cause}. Valid: {sorted(VALID_ROOT_CAUSES)}" | |
| if severity not in VALID_SEVERITIES: | |
| return -0.10, f"Invalid severity: {severity}. Valid: {sorted(VALID_SEVERITIES)}" | |
| if remediation not in VALID_REMEDIATIONS: | |
| return -0.10, f"Invalid remediation: {remediation}. Valid: {sorted(VALID_REMEDIATIONS)}" | |
| gt = self.ground_truth.get(alert_id, {}) | |
| reward = compute_triage_reward( | |
| root_cause, severity, remediation, | |
| gt.get("root_cause", ""), gt.get("severity", ""), | |
| gt.get("remediation", ""), | |
| alert_id, self.agent_incidents, self.incidents, | |
| ) | |
| alert["triaged"] = True | |
| alert["agent_decision"] = { | |
| "root_cause": root_cause, | |
| "severity": severity, | |
| "remediation": remediation, | |
| } | |
| self.agent_decisions[alert_id] = alert["agent_decision"] | |
| correct_parts = [] | |
| if root_cause == gt.get("root_cause"): | |
| correct_parts.append("root_cause") | |
| if severity == gt.get("severity"): | |
| correct_parts.append("severity") | |
| if remediation == gt.get("remediation"): | |
| correct_parts.append("remediation") | |
| feedback = f"Triaged {alert_id}: {len(correct_parts)}/3 components correct" | |
| return reward, feedback | |
| def _handle_link(self, action: Dict) -> Tuple[float, str]: | |
| alert_ids = action.get("alert_ids", []) | |
| incident_label = action.get("incident_label", "unknown") | |
| if len(alert_ids) < 2: | |
| return -0.10, "link_alerts requires at least 2 alert IDs" | |
| # Validate all alert IDs exist | |
| for aid in alert_ids: | |
| if not self._find_alert(aid): | |
| return -0.10, f"Unknown alert_id in link: {aid}" | |
| self.agent_incidents[incident_label] = set(alert_ids) | |
| reward = compute_link_reward(alert_ids, self.incidents) | |
| feedback = f"Linked {len(alert_ids)} alerts as '{incident_label}'" | |
| return reward, feedback | |
| def _handle_skip(self, action: Dict) -> Tuple[float, str]: | |
| alert_id = action.get("alert_id", "") | |
| alert = self._find_alert(alert_id) | |
| if not alert: | |
| return -0.10, f"Unknown alert_id: {alert_id}" | |
| if alert["triaged"]: | |
| return -0.15, f"Alert {alert_id} already triaged" | |
| gt = self.ground_truth.get(alert_id, {}) | |
| is_fa = gt.get("is_false_alarm", False) | |
| reward = compute_skip_reward(is_fa) | |
| alert["triaged"] = True | |
| alert["agent_decision"] = {"action": "skip"} | |
| self.skipped_alerts.add(alert_id) | |
| feedback = f"Skipped {alert_id}" + (" (correct — false alarm)" if is_fa else " (WRONG — real alert)") | |
| return reward, feedback | |
| def _trigger_cascade(self, additional: bool = False): | |
| """Spawn new dependent alerts for untriaged alerts. | |
| Args: | |
| additional: If True, this is the second wave (step 8+) - spawn more aggressively | |
| """ | |
| rng = random.Random(self.seed + 1000 + (100 if additional else 0)) | |
| base_time = datetime(2026, 4, 10, 3, 30, 0) | |
| new_alerts = [] | |
| # On first wave: spawn for all untriaged critical/high | |
| # On second wave (additional): also include medium, spawn more | |
| target_severities = CASCADE_SEVERITIES.copy() | |
| if additional: | |
| target_severities.add(Severity.MEDIUM) | |
| for alert in self.alerts: | |
| # Skip already spawned cascade alerts on first wave | |
| if not additional and alert["alert_id"] in self.cascade_spawned: | |
| continue | |
| if alert["triaged"]: | |
| continue | |
| gt = self.ground_truth.get(alert["alert_id"], {}) | |
| try: | |
| sev = Severity(gt.get("severity", "low")) | |
| except ValueError: | |
| continue | |
| if sev not in target_severities: | |
| continue | |
| # Find a dependent service | |
| dependents = get_dependents(alert["service"]) | |
| if not dependents: | |
| continue | |
| dep_svc = rng.choice(sorted(dependents)) | |
| self._alert_counter += 1 | |
| new_id = f"alert-{self._alert_counter:03d}" | |
| new_alert = { | |
| "alert_id": new_id, | |
| "timestamp": (base_time + timedelta(seconds=rng.randint(0, 120))).isoformat() + "Z", | |
| "service": dep_svc, | |
| "metric": "upstream_error_rate", | |
| "metric_value": round(30.0 + rng.uniform(0, 20), 2), | |
| "threshold": 10.0, | |
| "message": f"Cascade: upstream {alert['service']} failure causing errors in {dep_svc}", | |
| "context": f"Cascade from untriaged {alert['alert_id']}", | |
| "triaged": False, | |
| "agent_decision": None, | |
| } | |
| new_alerts.append(new_alert) | |
| self.cascade_spawned.add(new_id) | |
| # Ground truth for cascade alert | |
| self.ground_truth[new_id] = { | |
| "root_cause": RootCause.DEPENDENCY_OUTAGE.value, | |
| "severity": Severity.HIGH.value, | |
| "remediation": Remediation.ESCALATE_TO_TEAM.value, | |
| "is_false_alarm": False, | |
| "incident_label": gt.get("incident_label"), | |
| "is_stealth_root": False, | |
| } | |
| self.alerts.extend(new_alerts) | |