README.md

metadata

title: Taskmanager Environment Server
emoji: 🎬
colorFrom: green
colorTo: red
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
  - openenv
  - rl
  - scheduling

Taskmanager Environment

A reinforcement learning environment that simulates a real-world engineering workflow. The agent must prioritize tickets (bugs, features, UI enhancements) to maximize business impact and avoid SLA violations.

Quick Start

The simplest way to use the Taskmanager environment is through the TaskmanagerEnv class:

from taskmanager import TaskmanagerAction, TaskmanagerEnv

try:
    # Create environment from Docker image
    env = TaskmanagerEnv.from_docker_image("taskmanager:latest")

    # Reset to start a new episode
    result = env.reset()
    
    print(f"Current Time: {result.observation.current_time}")
    print(f"Available Tasks: {len(result.observation.tasks)}")

    # Execute tasks until the episode is done
    done = False
    while not done:
        # Simple policy: pick the first available task
        if not result.observation.tasks:
            break
            
        task_to_execute = result.observation.tasks[0]
        task_id = task_to_execute["id"]
        
        # Take a step
        result = env.step(TaskmanagerAction(task_id=task_id))
        
        print(f"Executed Task ID: {task_id}")
        print(f"  → Reward: {result.reward}")
        print(f"  → Current Time: {result.observation.current_time}")
        print(f"  → Tasks Remaining: {len(result.observation.tasks)}")
        
        done = result.done

    print("Episode completed!")

finally:
    # Always clean up
    env.close()

Environment Details

Action

TaskmanagerAction: Contains a single field specifying the task to execute.

• task_id (int) - The ID of the task/ticket to execute

Observation

TaskmanagerObservation: Contains the current state of the environment.

• tasks (List[Dict]) - List of remaining tickets. Each ticket has:
  • id (int): Unique identifier
  • type (str): "bug", "feature", or "enhancement"
  • effort (int): Time required to complete the ticket
  • priority (int): Importance (1-5)
  • deadline (int): Target completion time
• current_time (int) - Current time in the schedule
• steps_left (int) - Steps remaining in the episode
• reward (float) - Reward received from the previous action
• done (bool) - Whether the episode is complete (all tasks done or max steps reached)
• metadata (dict) - Additional info like step count

Reward Function

The reward function is designed to simulate business impact:

1. Base Reward: priority * 3 if completed before the deadline.
2. Penalty: If delayed, the reward is reduced based on the delay (priority - delay * 0.5), with a minimum of -2.
3. Type Multipliers:
   • Bugs: 2.0x multiplier (Critical)
   • Features: 1.5x multiplier
   • Enhancements: 1.0x multiplier
4. Invalid Action: -1 reward for attempting a non-existent task ID.

Building the Docker Image

Before using the environment, you need to build the Docker image.

To create the Docker image:

# From project root
docker build -t taskmanager .

To run the Docker image locally:

# Run the container in detached mode and map port 8000
docker run -d -p 8000:8000 --name taskmanager_server taskmanager

Deploying to Hugging Face Spaces

You can easily deploy your OpenEnv environment to Hugging Face Spaces using the openenv push command:

# From the environment directory (where openenv.yaml is located)
openenv push

# Or specify options
openenv push --namespace my-org --private

After deployment, your space will be available at: https://huggingface.co/spaces/<repo-id>

The deployed space includes:

• Web Interface at /web - Interactive UI for exploring the environment
• API Documentation at /docs - Full OpenAPI/Swagger interface
• Health Check at /health - Container health monitoring
• WebSocket at /ws - Persistent session endpoint for low-latency interactions

Project Structure

taskmanager/
├── client.py              # Environment client implementation
├── models.py              # Action and Observation Pydantic models
├── openenv.yaml           # OpenEnv manifest
├── server/
│   ├── app.py             # FastAPI application
│   └── taskmanager_environment.py  # Core environment logic and reward function
└── Dockerfile             # Container definition