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hard007ik/
shopmanager-train-code

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Link of the environment: https://huggingface.co/spaces/hard007ik/ShopManagerEng Link of Blog.md: https://huggingface.co/spaces/hard007ik/ShopManagerEng/tree/main/Blog.md

Jewelry Shop Manager — RL Environment

A reinforcement learning environment simulating a jewelry shop management pipeline. An AI agent navigates three sequential phases — buying raw materials, selecting products to craft based on demand, and negotiating sales — to maximize profit.

Environment Overview

Phase 1: Market (Buy / Wait)

Gold prices fluctuate ±10% each round (up to 3 rounds).
The agent analyzes price trends and decides to buy gold or wait for a better price.
Goal: Buy gold at the lowest possible price while reserving cash for crafting labor.

Phase 2: Warehouse (Product Selection)

The agent sees demand levels for each product type:

Product	Gold (oz)	Labor ($)	Demand Range
Ring	1.0	$200	40-100%
Necklace	2.0	$300	20-80%
Bracelet	0.5	$100	10-60%

The agent picks the highest-demand product it can afford to craft.
Goal: Match production to market demand.

Phase 3: Showroom (Negotiation)

A customer makes an initial offer based on cost basis and product demand.
The agent can accept, counter-offer, or reject.
Each counter raises the customer's offer by 5% (up to 5 rounds).
Goal: Sell at maximum profit through smart negotiation.

Reward Structure

Component	Weight	Description
R1 (Market)	20%	How close to the lowest price did the agent buy?
R2 (Warehouse)	20%	Did the agent pick the highest-demand product?
R3 (Showroom)	60%	Normalized profit margin on the sale

Final Score = 0.2 × R1 + 0.2 × R2 + 0.6 × R3 (range [0, 1])

Quick Start

python

from ShopManagerEng import JewelryAction, JewelryShopEnv

async def run():
    env = JewelryShopEnv(base_url="http://localhost:8000")

    result = await env.reset()
    print(f"Gold price: ${result.observation.gold_price}/oz")

    # Phase 1 — Market: wait for better price
    result = await env.step(JewelryAction(market_action="wait"))

    # Phase 1 — Market: buy gold
    result = await env.step(JewelryAction(market_action="buy", gold_qty=2.0))

    # Phase 2 — Warehouse: choose product
    result = await env.step(JewelryAction(product_choice="ring"))

    # Phase 3 — Showroom: negotiate
    result = await env.step(JewelryAction(message="How about $600?"))
    result = await env.step(JewelryAction(message="I accept"))

    print(f"Final reward: {result.reward}, Cash: {result.observation.cash}")
    await env.close()

import asyncio
asyncio.run(run())

Action Space

python

class JewelryAction:
    market_action:  str   # "buy" or "wait" (Phase 1)
    gold_qty:       float # Ounces to buy (Phase 1)
    product_choice: str   # "ring", "necklace", or "bracelet" (Phase 2)
    message:        str   # Negotiation text (Phase 3)

Observation Space

python

class JewelryObservation:
    phase:              str          # "market" | "warehouse" | "showroom"
    cash:               float        # Current cash balance
    gold_oz:            float        # Raw gold in inventory
    gold_price:         float        # Current gold price ($/oz)
    gold_price_history: List[float]  # Price trend for analysis
    market_round:       int          # Current market round
    demand:             Dict[str, float]  # Demand per product (0-1)
    product_catalog:    Dict[str, dict]   # Specs per product
    inventory:          Dict[str, int]    # Crafted products in stock
    product_for_sale:   str          # Product being sold (showroom)
    cost_basis:         float        # Total manufacturing cost
    current_offer:      float        # Customer's current offer
    negotiation_round:  int          # Counter-offer round
    message:            str          # Environment feedback

Running the Inference Script

bash

# Terminal 1: Start the server
cd ShopManagerEng
uv run server

# Terminal 2: Run inference (from parent directory or inside ShopManagerEng)
python inference.py

Required environment variables (set in .env):

HF_TOKEN — Hugging Face API token
MODEL_NAME — LLM model (default: meta-llama/Llama-3.3-70B-Instruct)

Deploying to Hugging Face Spaces

openenv push

Project Structure

ShopManagerEng/
├── __init__.py            # Module exports
├── README.md              # This file
├── openenv.yaml           # OpenEnv manifest
├── pyproject.toml         # Dependencies
├── models.py              # Action, Observation, State definitions
├── client.py              # JewelryShopEnv client
├── inference.py           # LLM-based agent inference script
└── server/
    ├── __init__.py
    ├── ShopManagerEng_environment.py  # Core environment logic
    ├── app.py             # FastAPI application
    └── Dockerfile         # Container image

hard007ik/shopmanager-train-code

Jewelry Shop Manager — RL Environment

Environment Overview

Phase 1: Market (Buy / Wait)

Phase 2: Warehouse (Product Selection)

Phase 3: Showroom (Negotiation)

Reward Structure

Quick Start

Action Space

Observation Space

Running the Inference Script

Deploying to Hugging Face Spaces

Project Structure

hard007ik/
shopmanager-train-code