priyaaaaaasharmaaaaa/trial1

🤖 AI Sprint Manager — OpenEnv

A reinforcement learning environment where an AI agent acts as a Tech Lead managing agile software sprints.

---

🎯 What Is This?

Modern software teams spend enormous time on sprint planning decisions:

• Which developer gets which task?
• What do you do when someone goes sick mid-sprint?
• How do you handle an urgent production bug that appears on day 5?

This environment simulates these real-world decisions so an AI agent can learn optimal sprint management strategies through reinforcement learning.

The agent plays the role of a Tech Lead. Each step it observes the full sprint state (tasks, developers, workloads, deadlines) and takes an action. The environment responds with a reward signal that guides learning.

---

🏗️ Architecture

┌─────────────────────────────────────────┐
│     RL Agent / LLM / Training Loop      │
│         (uses client.py)                │
└──────────────────┬──────────────────────┘
                   │ HTTP  reset / step / state
                   ▼
┌─────────────────────────────────────────┐
│         FastAPI Server (port 7860)      │
│    /reset  /step  /state  /health       │
└──────────────────┬──────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────┐
│      Sprint Environment (core logic)    │
│  • Task/developer simulation            │
│  • Reward calculation                   │
│  • Random events (bugs, absences)       │
│  • 3 graders: easy / medium / hard      │
└──────────────────┬──────────────────────┘
                   │ data loaded from
                   ▼
┌─────────────────────────────────────────┐
│      data/sprint_data.json              │
│  (customizable — bring your own data!)  │
└─────────────────────────────────────────┘

---

🎮 Live Demo

1. Select a sprint scenario (easy / medium / hard)
2. Click 🔄 Reset Sprint
3. Use the Skill → Dev Guide to assign tasks correctly
4. Or click 🤖 Auto-Assign All to let the system decide
5. Watch the reward history and task status update in real time

---

📐 Action Space

Field	Type	Values
action_type	string	assign, reassign, reprioritize, unblock, skip
task_id	string	Task ID e.g. "T1", "T6"
dev_id	string	Developer ID e.g. "dev1", "dev3"
new_priority	int	1–5 (1=highest, for reprioritize only)

📊 Observation Space

Field	Type	Description
current_day	int	Day in sprint (1–10)
sprint_length	int	Total sprint length
developers	list	Each dev's skill, capacity, load, tasks, availability
tasks	list	Each task's type, priority, effort, deadline, status, progress
reward	float	Step reward
cumulative_reward	float	Total reward this episode
tasks_completed/missed/in_progress/backlog	int	Status counts
workload_balance_score	float	0=unbalanced, 1=perfect
events	list	Events that just happened (completions, misses, absences)
done	bool	Whether episode is complete

---

🎯 Tasks (Scenarios)

ID	Difficulty	Devs	Tasks	Random Events
easy_sprint	🟢 Easy	3	5	None
medium_sprint	🟡 Medium	4	8	Dev absences, bugs expire
hard_sprint	🔴 Hard	5	12	Urgent bugs mid-sprint, cascading failures

Baseline Scores (meta-llama/Llama-3.1-8B-Instruct)

Task	Score
easy_sprint	0.01
medium_sprint	0.46 ████████
hard_sprint	0.01
Average	0.16

---

💰 Reward Function

Event	Reward
Assign task (skill match)	+0.8 to +1.3
Assign task (skill mismatch penalty)	+0.1 to +0.6
Wrong skill / over capacity	-0.15
Task completed on time	+0.5 to +2.5
Task completed late	+0.1
Task missed deadline	-0.3 to -1.5
Urgent bug missed	-0.25 extra
Skip (no action)	-0.05
Final score bonus	score × 10.0

---

🔌 API Reference

# Health check
GET /health → {"status": "ok", "env": "ai-sprint-manager"}

# Start new episode
POST /reset
Body: {"task_name": "easy_sprint", "seed": 42}

# Take one action
POST /step
Body: {"action": {"action_type": "assign", "task_id": "T1", "dev_id": "dev1"}}

# Get full state
GET /state

# List scenarios
GET /tasks

---

🐍 Python Client Usage

from client import SprintEnvClient
from sprint_env.models import SprintAction

# Connect to live Space
with SprintEnvClient(base_url="https://sejal-k-ai-sprint-manager.hf.space") as env:
    # Reset
    obs = env.reset(task_name="medium_sprint", seed=42)

    # Agent loop
    while not obs["done"]:
        action = SprintAction(
            action_type="assign",
            task_id="T1",
            dev_id="dev1",
        )
        result = env.step(action)
        print(result)  # StepResult(reward=+1.20, done=False, day=2, completed=0)
        obs = result.observation

---

🗂️ Project Structure

ai-sprint-manager-openenv/
├── openenv.yaml              # OpenEnv spec metadata
├── pyproject.toml            # Project dependencies
├── Dockerfile                # Container definition
├── requirements.txt          # Python dependencies
├── inference.py              # Baseline LLM agent script
├── client.py                 # Typed Python client (for RL training)
├── ui.py                     # Gradio UI + FastAPI combined server
├── start.sh                  # Container startup script
│
├── data/
│   └── sprint_data.json      # All scenario data (customizable!)
│
├── sprint_env/
│   ├── __init__.py
│   ├── models.py             # Pydantic Action/Observation/State
│   ├── tasks.py              # Task & Developer dataclasses
│   ├── environment.py        # Core RL environment logic
│   ├── graders.py            # Scoring functions (easy/medium/hard)
│   └── data_loader.py        # JSON data loader with caching
│
└── server/
    ├── __init__.py
    └── app.py                # OpenEnv-compliant FastAPI server entry

---

🔧 Bring Your Own Data

Don't want to use our sample scenarios? Edit data/sprint_data.json:

{
  "scenarios": {
    "my_custom_sprint": {
      "description": "My team's actual sprint",
      "difficulty": "medium",
      "developers": [
        {"id": "dev1", "name": "Your Name", "skill": "backend", "capacity": 5, "productivity": 1.0}
      ],
      "tasks": [
        {"id": "T1", "name": "Your Task", "task_type": "feature", "priority": 1,
         "effort": 3, "deadline": 5, "required_skill": "backend"}
      ]
    }
  }
}

Or point to your own file:

export SPRINT_DATA_PATH=/path/to/your/data.json
python ui.py

---

🚀 Setup & Run

# Clone
git clone https://github.com/sejalsksagar/ai-sprint-manager-openenv.git
cd ai-sprint-manager-openenv

# Install
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Configure
cp .env.example .env
# Edit .env with your HF_TOKEN

# Run locally
python ui.py
# Open http://localhost:7860

# Docker
docker build -t ai-sprint-manager .
docker run -p 7860:7860 ai-sprint-manager

# Run inference
python inference.py

---

🤖 Can an RL Agent Learn From This?

Yes. The environment is designed for policy gradient training (GRPO, PPO):

# Example training loop skeleton (TRL/GRPO compatible)
from client import SprintEnvClient
from sprint_env.models import SprintAction

env = SprintEnvClient(base_url="http://localhost:7860")

for episode in range(1000):
    obs = env.reset(task_name="medium_sprint")
    trajectory = []

    while not obs["done"]:
        action = policy.sample(obs)           # your policy here
        result = env.step(action)
        trajectory.append((obs, action, result.reward))
        obs = result.observation

    policy.update(trajectory)                 # GRPO/PPO update

The shaped reward function provides learning signal at every step — not just at episode end — which is critical for efficient RL training.

---

👥 Team

Built for the Meta PyTorch OpenEnv Hackathon x SST | India AI Hackathon '26