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Palak6106/email-sorting-openenv

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App README

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:

json

{
  "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

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

bash

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

bash

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

API

bash

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	—