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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the BSD-style license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """ | |
| Task configurations and graders for the Store Manager environment. | |
| Four difficulty levels are defined, in strict increasing order of complexity: | |
| - easy : 4 products, 20 steps — learn discount mechanics, no expiry pressure | |
| - medium : 8 products, 28 steps — discount + zone placement (zone 1 is contested) | |
| - hard : 10 products, 30 steps — discount + zone + expiry timing (most expire mid-episode) | |
| - expert : 12 products, 25 steps — all three actions required (restock essential, aggressive expiry) | |
| Each task has a fixed seed so grading is deterministic: the same agent always | |
| produces the same episode given the same seed, and the score is computed as: | |
| score = clamp(cumulative_profit / profit_target, 0.001, 0.999) | |
| """ | |
| from __future__ import annotations | |
| from dataclasses import dataclass | |
| from typing import Dict | |
| class TaskConfig: | |
| """Configuration for one difficulty level of the Store Manager task.""" | |
| name: str | |
| description: str | |
| num_products: int | |
| max_steps: int | |
| num_customers: int | |
| expiry_offset_min: int # min steps before a product expires | |
| expiry_offset_max: int # max steps before a product expires | |
| seed: int # fixed seed for deterministic episode generation | |
| profit_target: float # cumulative profit at which grade == 1.0 | |
| # ── Canonical task registry ──────────────────────────────────────────────────── | |
| TASKS: Dict[str, TaskConfig] = { | |
| "easy": TaskConfig( | |
| name="easy", | |
| description=( | |
| "4 products, 20 steps, 15 customers/step. " | |
| "Products expire well after the episode ends — no expiry pressure. " | |
| "Focus on learning the discount mechanic to boost high-margin products. " | |
| "Zone placement is available but low-stakes; restocking is unnecessary." | |
| ), | |
| num_products=4, | |
| max_steps=20, | |
| num_customers=15, | |
| expiry_offset_min=18, | |
| expiry_offset_max=30, | |
| seed=42, | |
| profit_target=120.0, | |
| ), | |
| "medium": TaskConfig( | |
| name="medium", | |
| description=( | |
| "8 products, 28 steps, 12 customers/step. " | |
| "Several products expire mid-episode (12-22 steps). " | |
| "Zone 1 has only 2 slots for 8 competing products — " | |
| "combine targeted discounts with zone placement to maximize clearance " | |
| "before expiry. Restocking is rarely necessary." | |
| ), | |
| num_products=8, | |
| max_steps=28, | |
| num_customers=12, | |
| expiry_offset_min=12, | |
| expiry_offset_max=22, | |
| seed=200, | |
| profit_target=200.0, | |
| ), | |
| "hard": TaskConfig( | |
| name="hard", | |
| description=( | |
| "10 products, 30 steps, 10 customers/step. " | |
| "Most products expire in 8-18 steps — expiry management is critical. " | |
| "Zone strategy (rotate zone 1 for near-expiry items) combined with " | |
| "discounts drives clearance. Restock fast-sellers that run out early " | |
| "to maintain revenue through the full episode." | |
| ), | |
| num_products=10, | |
| max_steps=30, | |
| num_customers=10, | |
| expiry_offset_min=8, | |
| expiry_offset_max=18, | |
| seed=456, | |
| profit_target=240.0, | |
| ), | |
| "expert": TaskConfig( | |
| name="expert", | |
| description=( | |
| "12 products, 25 steps, 8 customers/step. " | |
| "Aggressive expiry (4-10 steps) with limited initial stock — " | |
| "all three actions must be coordinated: discount near-expiry items to " | |
| "clear them fast, restock high-margin products before they stock out, " | |
| "and continuously rotate zone 1 to prioritise the most urgent products." | |
| ), | |
| num_products=12, | |
| max_steps=25, | |
| num_customers=8, | |
| expiry_offset_min=4, | |
| expiry_offset_max=10, | |
| seed=789, | |
| profit_target=190.0, | |
| ), | |
| } | |
| # ── Grader ───────────────────────────────────────────────────────────────────── | |
| def grade(task_name: str, cumulative_profit: float) -> float: | |
| """ | |
| Return a deterministic score in (0.0, 1.0) exclusive for a completed episode. | |
| Formula: | |
| score = clamp(cumulative_profit / profit_target, 0.001, 0.999) | |
| Args: | |
| task_name: One of "easy", "medium", "hard", "expert" (or a custom string). | |
| cumulative_profit: Total profit accumulated over the episode. | |
| Returns: | |
| Score strictly between 0.0 and 1.0 (exclusive). | |
| """ | |
| import math | |
| _EPS = 0.001 | |
| try: | |
| cp = float(cumulative_profit) | |
| if not math.isfinite(cp): | |
| cp = 0.0 | |
| except (TypeError, ValueError): | |
| cp = 0.0 | |
| config = TASKS.get(task_name) | |
| if config is None or config.profit_target <= 0: | |
| return 1.0 - _EPS | |
| return min(1.0 - _EPS, max(_EPS, cp / config.profit_target)) | |
| __all__ = ["TaskConfig", "TASKS", "grade"] | |