Update README.md

README.md

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-# 📦 LogisticsFlow-OpenEnv
-
-A real-world OpenEnv simulation for testing AI agents on Supply Chain Management, Budgeting, and Resource Allocation.
-
-## 🎯 The Real-World Problem
-Modern E-commerce fulfillment requires balancing strict budgets with customer satisfaction. AI agents must learn to prioritize high-value VIP orders, manage limited budgets, and anticipate stock-outs before they happen. 
-
-This environment simulates a live warehouse dispatch system. Agents are not just playing a game; they are optimizing a simulated business.
-
-## 🧠 Environment Features
-* **Strict OpenEnv Compliance:** Fully typed `Action` and `Observation` models using Pydantic.
-* **Continuous Partial Rewards:** Agents receive granular reward signals for every successful shipment, not just a binary score at the end.
-* **Dynamic State Degradation:** Customer satisfaction drops as orders age in the queue, forcing the agent to act efficiently.
-* **Deterministic Task Graders:** Built-in programmatic graders (Easy, Medium, Hard) that return a strict 0.0 to 1.0 score based on budget retention and order completion.
-
-## 🚀 Tasks & Difficulty
-1. **Easy (`/reset/easy`):** Basic capability test. Agent must ship existing inventory.
-2. **Medium (`/reset/medium`):** Constraint optimization. Agent is given a severely limited budget and must choose the cheapest shipping carriers to survive.
-3. **Hard (`/reset/hard`):** Multi-step reasoning. The agent is faced with a "Stock-out Crisis" (0 inventory). It must realize the shortage, execute a `restock` command, and *then* fulfill the orders.
-
-## 🛠️ Action Space
-* `ship`: Dispatch an order (Requires `order_id` and `carrier`).
-* `restock`: Purchase more inventory to fulfill future orders (Requires `item`).
-
-## 📊 Observation Space
-* `inventory`: Current stock counts (e.g., `{"Electronics": 10}`).
-* `pending_orders`: Queue of orders with priority levels and age.
-* `budget`: Available capital.
-
-## 🏃 How to Run Locally
-1. Install dependencies: `pip install -r requirements.txt`
-2. Start the server: `python -m uvicorn main:app --port 7860`
+---
+title: LogisticsFlow OpenEnv
+emoji: 📦
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+---
+# 📦 LogisticsFlow-OpenEnv
+
+A real-world OpenEnv simulation for testing AI agents on Supply Chain Management, Budgeting, and Resource Allocation.
+
+## 🎯 The Real-World Problem
+Modern E-commerce fulfillment requires balancing strict budgets with customer satisfaction. AI agents must learn to prioritize high-value VIP orders, manage limited budgets, and anticipate stock-outs before they happen. 
+
+This environment simulates a live warehouse dispatch system. Agents are not just playing a game; they are optimizing a simulated business.
+
+## 🧠 Environment Features
+* **Strict OpenEnv Compliance:** Fully typed `Action` and `Observation` models using Pydantic.
+* **Continuous Partial Rewards:** Agents receive granular reward signals for every successful shipment, not just a binary score at the end.
+* **Dynamic State Degradation:** Customer satisfaction drops as orders age in the queue, forcing the agent to act efficiently.
+* **Deterministic Task Graders:** Built-in programmatic graders (Easy, Medium, Hard) that return a strict 0.0 to 1.0 score based on budget retention and order completion.
+
+## 🚀 Tasks & Difficulty
+1. **Easy (`/reset/easy`):** Basic capability test. Agent must ship existing inventory.
+2. **Medium (`/reset/medium`):** Constraint optimization. Agent is given a severely limited budget and must choose the cheapest shipping carriers to survive.
+3. **Hard (`/reset/hard`):** Multi-step reasoning. The agent is faced with a "Stock-out Crisis" (0 inventory). It must realize the shortage, execute a `restock` command, and *then* fulfill the orders.
+
+## 🛠️ Action Space
+* `ship`: Dispatch an order (Requires `order_id` and `carrier`).
+* `restock`: Purchase more inventory to fulfill future orders (Requires `item`).
+
+## 📊 Observation Space
+* `inventory`: Current stock counts (e.g., `{"Electronics": 10}`).
+* `pending_orders`: Queue of orders with priority levels and age.
+* `budget`: Available capital.
+
+## 🏃 How to Run Locally
+1. Install dependencies: `pip install -r requirements.txt`
+2. Start the server: `python -m uvicorn main:app --port 7860`
 3. The environment will be available at `http://localhost:7860`