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ML policy helpers for flight rebooking.
This module provides:
- deterministic expert policy used for dataset generation,
- fixed-length feature extraction for supervised learning,
- safe action construction from ranked action-type preferences.
"""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional
from environment import ActionType, CabinClass, PriorityTier
ACTION_TYPE_ORDER: List[str] = [
ActionType.REBOOK_PASSENGER.value,
ActionType.OFFER_DOWNGRADE.value,
ActionType.REBOOK_ON_PARTNER.value,
ActionType.BOOK_HOTEL.value,
ActionType.MARK_NO_SOLUTION.value,
ActionType.FINALIZE.value,
]
def _tier_weight(tier: str) -> int:
return {
PriorityTier.PLATINUM.value: 4,
PriorityTier.GOLD.value: 3,
PriorityTier.SILVER.value: 2,
PriorityTier.STANDARD.value: 1,
}.get(tier, 1)
def _deadline_sort_value(deadline_hrs: Optional[float]) -> float:
return float(deadline_hrs) if deadline_hrs is not None else 10**9
def _has_seat(flight: Dict[str, Any], cabin_class: str) -> bool:
if cabin_class == CabinClass.BUSINESS.value:
return int(flight["business_seats"]) > 0
return int(flight["economy_seats"]) > 0
def _sorted_pending_passengers(observation: Dict[str, Any]) -> List[Dict[str, Any]]:
pending = list(observation.get("pending_passengers", []))
pending.sort(
key=lambda p: (
-_tier_weight(str(p.get("priority_tier", ""))),
_deadline_sort_value(p.get("connection_deadline_hrs")),
)
)
return pending
def _sorted_flights(observation: Dict[str, Any]) -> List[Dict[str, Any]]:
flights = list(observation.get("available_flights", []))
flights.sort(key=lambda f: float(f.get("departure_hrs", 10**9)))
return flights
def heuristic_action(observation: Dict[str, Any]) -> Dict[str, Any]:
pending = _sorted_pending_passengers(observation)
if not pending:
return {"action_type": ActionType.FINALIZE.value}
passenger = pending[0]
flights = _sorted_flights(observation)
for flight in flights:
if flight.get("is_partner", False):
continue
if _has_seat(flight, str(passenger["cabin_class"])):
return {
"action_type": ActionType.REBOOK_PASSENGER.value,
"passenger_id": passenger["id"],
"flight_id": flight["id"],
}
if passenger.get("cabin_class") == CabinClass.BUSINESS.value:
for flight in flights:
if flight.get("is_partner", False):
continue
if int(flight.get("economy_seats", 0)) > 0 and float(observation.get("budget_remaining", 0.0)) >= 500.0:
return {
"action_type": ActionType.OFFER_DOWNGRADE.value,
"passenger_id": passenger["id"],
"flight_id": flight["id"],
}
for flight in flights:
if not flight.get("is_partner", False):
continue
if _has_seat(flight, str(passenger["cabin_class"])) and float(observation.get("budget_remaining", 0.0)) >= 800.0:
return {
"action_type": ActionType.REBOOK_ON_PARTNER.value,
"passenger_id": passenger["id"],
"flight_id": flight["id"],
}
if float(observation.get("budget_remaining", 0.0)) >= 250.0:
return {
"action_type": ActionType.BOOK_HOTEL.value,
"passenger_id": passenger["id"],
}
return {
"action_type": ActionType.MARK_NO_SOLUTION.value,
"passenger_id": passenger["id"],
}
def observation_to_features(
observation: Dict[str, Any],
max_pending: int = 5,
max_flights: int = 6,
) -> List[float]:
pending = _sorted_pending_passengers(observation)
flights = _sorted_flights(observation)
processed_count = float(observation.get("processed_count", 0))
total_passengers = max(float(observation.get("total_passengers", 1)), 1.0)
budget_remaining = max(float(observation.get("budget_remaining", 0.0)), 0.0)
budget_spent = max(float(observation.get("budget_spent", 0.0)), 0.0)
budget_total = max(budget_remaining + budget_spent, 1.0)
features: List[float] = []
features.extend(
[
min(len(pending), 20) / 20.0,
min(len(flights), 20) / 20.0,
budget_remaining / budget_total,
budget_spent / budget_total,
processed_count / total_passengers,
min(float(observation.get("invalid_actions", 0)), 20.0) / 20.0,
min(float(observation.get("step_count", 0)), 120.0) / 120.0,
1.0 if pending else 0.0,
]
)
for passenger in pending[:max_pending]:
deadline = passenger.get("connection_deadline_hrs")
has_deadline = 1.0 if deadline is not None else 0.0
deadline_norm = (min(float(deadline), 12.0) / 12.0) if deadline is not None else 1.0
features.extend(
[
_tier_weight(str(passenger.get("priority_tier", ""))) / 4.0,
1.0 if passenger.get("cabin_class") == CabinClass.BUSINESS.value else 0.0,
has_deadline,
deadline_norm,
]
)
for _ in range(max_pending - len(pending[:max_pending])):
features.extend([0.0, 0.0, 0.0, 0.0])
for flight in flights[:max_flights]:
features.extend(
[
1.0 if flight.get("is_partner", False) else 0.0,
min(float(flight.get("departure_hrs", 12.0)), 12.0) / 12.0,
min(float(flight.get("economy_seats", 0.0)), 12.0) / 12.0,
min(float(flight.get("business_seats", 0.0)), 6.0) / 6.0,
]
)
for _ in range(max_flights - len(flights[:max_flights])):
features.extend([0.0, 0.0, 0.0, 0.0])
same_econ = 0.0
same_bus = 0.0
partner_econ = 0.0
partner_bus = 0.0
for flight in flights:
if flight.get("is_partner", False):
partner_econ += float(flight.get("economy_seats", 0.0))
partner_bus += float(flight.get("business_seats", 0.0))
else:
same_econ += float(flight.get("economy_seats", 0.0))
same_bus += float(flight.get("business_seats", 0.0))
features.extend(
[
min(same_econ, 30.0) / 30.0,
min(same_bus, 20.0) / 20.0,
min(partner_econ, 30.0) / 30.0,
min(partner_bus, 20.0) / 20.0,
]
)
return features
def build_feasible_action_for_type(observation: Dict[str, Any], action_type: str) -> Optional[Dict[str, Any]]:
pending = _sorted_pending_passengers(observation)
flights = _sorted_flights(observation)
budget_remaining = float(observation.get("budget_remaining", 0.0))
if not pending:
return {"action_type": ActionType.FINALIZE.value}
if action_type == ActionType.FINALIZE.value:
return None
if action_type == ActionType.BOOK_HOTEL.value:
if budget_remaining >= 250.0:
return {
"action_type": ActionType.BOOK_HOTEL.value,
"passenger_id": pending[0]["id"],
}
return None
if action_type == ActionType.MARK_NO_SOLUTION.value:
return {
"action_type": ActionType.MARK_NO_SOLUTION.value,
"passenger_id": pending[0]["id"],
}
if action_type == ActionType.REBOOK_PASSENGER.value:
for passenger in pending:
for flight in flights:
if flight.get("is_partner", False):
continue
if _has_seat(flight, str(passenger["cabin_class"])):
return {
"action_type": ActionType.REBOOK_PASSENGER.value,
"passenger_id": passenger["id"],
"flight_id": flight["id"],
}
return None
if action_type == ActionType.OFFER_DOWNGRADE.value:
if budget_remaining < 500.0:
return None
business_pending = [p for p in pending if p.get("cabin_class") == CabinClass.BUSINESS.value]
for passenger in business_pending:
for flight in flights:
if flight.get("is_partner", False):
continue
if int(flight.get("economy_seats", 0)) > 0:
return {
"action_type": ActionType.OFFER_DOWNGRADE.value,
"passenger_id": passenger["id"],
"flight_id": flight["id"],
}
return None
if action_type == ActionType.REBOOK_ON_PARTNER.value:
if budget_remaining < 800.0:
return None
for passenger in pending:
for flight in flights:
if not flight.get("is_partner", False):
continue
if _has_seat(flight, str(passenger["cabin_class"])):
return {
"action_type": ActionType.REBOOK_ON_PARTNER.value,
"passenger_id": passenger["id"],
"flight_id": flight["id"],
}
return None
return None
def choose_action_from_ranked_types(observation: Dict[str, Any], ranked_types: Iterable[str]) -> Dict[str, Any]:
for action_type in ranked_types:
if not action_type:
continue
candidate = build_feasible_action_for_type(observation, str(action_type))
if candidate is not None:
return candidate
return heuristic_action(observation)
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