lakshminarayananp/openenv-auxon-inventory-management

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Auxon Inventory Optimization — RL Environment

This repository contains a complete, production-ready Reinforcement Learning (RL) environment for optimizing inventory management in an e-commerce context.

🎯 Project Goal

The objective is to maximize total profit while balancing stock levels, pricing, and warehouse capacity constraints. The agent must navigate stochastic demand influenced by pricing decisions.

🧠 RL Design

State (Observation)

Each step returns a Observation model:

• —product_id: Unique identifier
• —current_stock: Units available
• —demand_rate: Predicted demand for the step
• —product_price: Current selling price
• —storage_cost_per_unit: Cost to hold one unit
• —warehouse_capacity_remaining: Available space
• —days_left: Time steps remaining in episode
• —unmet_demand: Stockout quantity from previous step
• —holding_cost: Total cost for current stock

Action Space

• —restock(quantity): Increase inventory (at 50% base price cost)
• —reduce_price(percentage): Increase demand, lower margin
• —increase_price(percentage): Decrease demand, higher margin
• —transfer_warehouse(quantity): Move stock (at fixed cost)
• —do_nothing: No change

Reward Function

Reward = Sales_Revenue - Restock_Cost - Storage_Cost - Stockout_Penalty - Overstock_Penalty - Transfer_Cost

🧪 Scenarios & Grading

The environment supports three difficulty levels in scenarios.py:

1. 1.Easy (Auxon Basics): Low fluctuation, stable demand.
2. 2.Medium (Seasonal): Higher demand variance.
3. 3.Hard (Mega Sale Day/Festival): Extreme demand spikes and tight capacity.

Grading: Each scenario calculates a normalized score (0 to 1) based on cumulative profit targets.

🚀 Setup & Execution

Local Development

1. 1.Install dependencies:

   pip install -r requirements.txt

1. 1.Run the Dashboard:

   python app.py

1. 1.Open http://localhost:8000 in your browser.

Docker

docker build -t auxon-inventory-rl .
docker run -p 7860:7860 auxon-inventory-rl

Inference Log Format

To run a strict-format inference (for OpenEnv evaluation):

python inference.py

🧩 Technical Stack

• —Backend: FastAPI, Pydantic, NumPy
• —Frontend: Vanilla HTML5, CSS3 (Glassmorphic), JavaScript
• —Visualization: Chart.js
• —Spec: OpenEnv v1.0