name: ai-executive-assistant
version: "1.0"
entry_point: env.assistant_env:ExecutiveAssistantEnv

observation_space:
  type: dict
  keys:
    time:
      type: string
      description: Current simulation time in HH:MM format
    tasks:
      type: list
      description: List of task objects with id, title, time, duration, priority, type, status
    inbox:
      type: list
      description: List of inbox message objects with id, sender, content, urgency, replied
    preferences:
      type: dict
      description: User preference profile for personalization

action_space:
  type: discrete
  actions:
    - schedule_task
    - complete_task
    - defer_task
    - send_reply
    - reject_task
    - ask_clarification

max_steps: 50
reward_range: [-20, 20]

features:
  temporal_reasoning: true
  partial_observability: true
  action_masking: true
  curriculum_learning: true
  conflict_graph: true
  user_preferences: true

description: >
  RL environment simulating an executive assistant handling scheduling,
  inbox communication, and task prioritization. Features temporal reasoning
  with overlap detection, multi-objective reward shaping, partial observability
  with hidden tasks and delayed inbox, action masking, conflict graph modeling,
  curriculum learning, and personalization via user preference memory.