Upload README.txt

README.txt

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+# 🛒 E-commerce Recommendation RL Environment (OpenEnv)
+
+## 🚀 Overview
+This project simulates a real-world e-commerce recommendation system where an AI agent suggests products to users and learns from interactions such as clicks and purchases.
+
+The goal is to optimize user engagement and conversion rates using reinforcement learning.
+
+---
+
+## 🎯 Problem Statement
+Recommender systems are critical in real-world platforms like Amazon and Flipkart. This environment allows training agents to make optimal recommendations under uncertainty.
+
+---
+
+## 🧠 Environment Design
+
+### Observation Space
+- User browsing history
+- Previously recommended products
+
+### Action Space
+- Recommend a product ID
+
+### Reward Function
+- +1 → Purchase
+- +0.3 → Click
+- 0 → Ignore
+
+---
+
+## 🧪 Tasks
+
+### Easy
+- Predict obvious user preference
+
+### Medium
+- Handle mixed user behavior
+
+### Hard
+- Optimize over multiple steps
+
+---
+
+## ⚙️ OpenEnv API
+
+- `reset()` → Initialize session  
+- `step(action)` → Returns observation, reward, done  
+- `state()` → Current environment state  
+
+---
+
+## 📊 Baseline Results
+
+Example:
+Episode reward: 1.0 (optimal recommendation)
+
+---
+
+## 🖥️ Demo
+
+👉 Hugging Face Space: [YOUR LINK]
+
+---
+
+## 🐳 Setup
+
+```bash
+pip install -r requirements.txt
+python app.py