| """
|
| Grader for inventory optimization tasks.
|
| Scores agent performance on a 0.0-1.0 scale using floor/ceiling approach.
|
| - floor: passive agent (no buys, just sells initial stock until empty)
|
| - ceiling: theoretical max profit with perfect demand knowledge
|
| """
|
|
|
| from server.inventory_env import InventoryEnvironment
|
| from models import InventoryAction
|
| from server.constants import (
|
| TASKS, BASE_PRICES, COST_PRICES, SHIPPING_COST, EVENT_EFFECTS,
|
| WEEKEND_MULTIPLIER, EVENT_DURATION,
|
| )
|
|
|
| import random
|
|
|
|
|
| def _run_passive(task_name):
|
| """Floor baseline: do nothing, just sell whatever initial stock covers."""
|
| env = InventoryEnvironment(task_name)
|
| obs = env.reset()
|
|
|
| while not obs.done:
|
| action = InventoryAction(
|
| buy_quantities={},
|
| delivery_methods={},
|
| liquidate={},
|
| )
|
| obs = env.step(action)
|
|
|
| return obs.total_profit
|
|
|
|
|
| def _run_heuristic(task_name):
|
| task = TASKS[task_name]
|
| events = dict(task["events"])
|
|
|
| total_demand = {p: 0 for p in task["base_demand"]}
|
|
|
| for day in range(1, task["max_days"] + 1):
|
|
|
| for event_name in events:
|
| events[event_name] -= 1
|
|
|
| rng = random.Random(task["seed"] * 1000 + day)
|
|
|
| for product, (lo, hi) in task["base_demand"].items():
|
| demand = rng.randint(lo, hi)
|
|
|
|
|
| if day % 7 == 5 or day % 7 == 6:
|
| demand = int(WEEKEND_MULTIPLIER * demand)
|
|
|
|
|
| for event_name, days_left in events.items():
|
| if -EVENT_DURATION < days_left <= 0 and event_name in EVENT_EFFECTS:
|
| mult = EVENT_EFFECTS[event_name].get(product, 1.0)
|
| demand = int(demand * mult)
|
|
|
| total_demand[product] += demand
|
|
|
| total_profit = 0.0
|
|
|
|
|
| initial_stock = task["initial_stock"]
|
|
|
| for product in task["base_demand"]:
|
| total_profit += min(initial_stock.get(product, 0), total_demand[product]) * BASE_PRICES[product]
|
| total_demand[product] = max(0, total_demand[product] - initial_stock.get(product, 0))
|
|
|
|
|
| total_profit += total_demand[product] * (BASE_PRICES[product] - COST_PRICES[product] - SHIPPING_COST["slow"])
|
|
|
| return total_profit
|
|
|
|
|
| def compute_baselines(task_name):
|
| """Pre-compute floor and ceiling for a task."""
|
| floor = _run_passive(task_name)
|
| ceiling = _run_heuristic(task_name)
|
| return floor, ceiling
|
|
|
|
|
| def grade(task_name, agent_profit):
|
| """
|
| Grade agent performance on 0.0-1.0 scale.
|
|
|
| Args:
|
| task_name: "easy", "medium", or "hard"
|
| agent_profit: total profit achieved by the agent
|
|
|
| Returns:
|
| float score between 0.0 and 1.0
|
| """
|
| floor, ceiling = compute_baselines(task_name)
|
|
|
| if ceiling <= floor:
|
| return 1.0 if agent_profit >= ceiling else 0.0
|
|
|
| score = (agent_profit - floor) / (ceiling - floor)
|
| return max(0.002, min(0.998, score))
|
|
|
|
|
| def grade_all(results):
|
| """
|
| Grade all 3 tasks.
|
|
|
| Args:
|
| results: dict of {task_name: agent_profit}
|
|
|
| Returns:
|
| dict of {task_name: score}
|
| """
|
| scores = {}
|
| for task_name, agent_profit in results.items():
|
| scores[task_name] = grade(task_name, agent_profit)
|
| return scores
|
|
|
|
|
| if __name__ == "__main__":
|
| print("Computing baselines for all tasks...")
|
| for task_name in ["easy", "medium", "hard"]:
|
| floor, ceiling = compute_baselines(task_name)
|
| print(f" {task_name}: floor={floor:.2f}, ceiling={ceiling:.2f}") |
