name: ReproAgent
version: 1.0.0
description: AI agent that automatically reproduces ML research papers

environment:
  id: reproagent-v1
  entry_point: reproagent.environment:ReproAgentEnv
  
observation_space:
  type: Dict
  spaces:
    paper_features:
      type: Box
      low: 0.0
      high: 1.0
      shape: [5]
      dtype: float32
    repo_features:
      type: Box
      low: 0.0
      high: 1.0
      shape: [5]
      dtype: float32
    execution_features:
      type: Box
      low: 0.0
      high: 1.0
      shape: [5]
      dtype: float32
    experiment_features:
      type: Box
      low: 0.0
      high: 1.0
      shape: [5]
      dtype: float32
    meta_features:
      type: Box
      low: 0.0
      high: 1.0
      shape: [5]
      dtype: float32

action_space:
  type: Discrete
  n: 50

reward_range:
  min: -100
  max: 200

max_episode_steps: 100

tasks:
  - name: easy
    description: "Clean repository with good documentation, runs first time"
    difficulty: 1
    success_threshold: 0.95
    
  - name: medium
    description: "Repository needs debugging and dependency fixes"
    difficulty: 2
    success_threshold: 0.90
    
  - name: hard
    description: "No code available, must implement from scratch"
    difficulty: 3
    success_threshold: 0.85

metadata:
  author: ReproAgent Team
  license: MIT
  tags:
    - research
    - reproduction
    - machine-learning
    - debugging
    - hyperparameter-tuning
  frameworks:
    - pytorch
    - tensorflow
    - jax
  version: 1.0.0
  creation_date: "2024"