README.md

metadata

title: Email Sorting Openenv
emoji: 📧
colorFrom: blue
colorTo: red
sdk: docker
pinned: false
tags:
  - openenv

Email Sorting OpenEnv 📧

A real-world OpenEnv environment where an AI agent learns to classify emails as spam, important, or promotion. Built for the Meta x PyTorch OpenEnv Hackathon.

---

What This Project Does

In today's world, people receive hundreds of emails daily. This environment trains an AI agent to automatically sort emails by reading the subject, body, and sender — just like a smart inbox.

The agent learns:

• Correct classification = reward
• Wrong classification = penalty
• Harder emails = higher reward (to encourage learning)

---

Environment Details

Property	Value
Task Type	Email Classification
Action Space	spam / important / promotion
Max Steps	10 per episode
Reward Range	-0.5 to +1.0
Difficulty Levels	Easy / Medium / Hard

---

Action Space

The agent can take exactly one of these actions per step:

Action	Meaning
spam	Email is unwanted, scam, or phishing
important	Email needs attention (work, orders, real alerts)
promotion	Genuine sale or discount from real shops

---

Observation Space

Each step the agent receives:

{
  "email": {
    "subject": "You won $1,000,000!",
    "body": "Click here to claim your prize.",
    "sender": "prize@randomsite.xyz"
  },
  "step": 1,
  "max_steps": 10,
  "total_reward": 0.0,
  "done": false,
  "valid_actions": ["spam", "important", "promotion"]
}

---

Tasks

Task	Difficulty	Description	Expected Score
easy_sorting	Easy	Classify obvious spam vs important emails	0.6 – 0.8
medium_sorting	Medium	Distinguish spam, promotion, and important	0.5 – 0.7
hard_sorting	Hard	Detect subtle phishing and tricky promotions	0.4 – 0.6

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Reward Function

Event	Reward
Easy correct	+0.5
Medium correct	+0.75
Hard correct	+1.0
Easy wrong	-0.5
Medium wrong	-0.3
Hard wrong	-0.1
Invalid action	-0.2

---

Baseline Scores

Scores achieved by the rule-based baseline agent (baseline_agent in graders.py):

Task	Score
easy_sorting	0.8
medium_sorting	0.67
hard_sorting	0.5
Average	0.657

---

Setup & Usage

Local

pip install -r requirements.txt
python server.py           # starts server at http://localhost:7860
python graders.py          # run graders with baseline agent
python inference.py        # run inference loop

Docker

docker build -t email-sorting-openenv .
docker run -p 7860:7860 email-sorting-openenv

API

curl -X POST http://localhost:7860/reset
curl -X POST http://localhost:7860/step -H "Content-Type: application/json" -d '{"action":"spam"}'
curl http://localhost:7860/state
curl http://localhost:7860/graders

Environment Variables

Variable	Description	Default
API_BASE_URL	LLM API base URL	https://api.openai.com/v1
MODEL_NAME	Model to use	gpt-4o-mini
HF_TOKEN	HuggingFace / API token	—