prd_adaptive_ui_layout_optimization_environment_final_enhanced.md

Product Requirements Document (PRD)

Product Name

Adaptive UI Layout Optimization Environment (OpenEnv)

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1. Problem Statement

Static A/B testing cannot adapt UI layouts per user in real time, leading to suboptimal conversions and user experience. We need a standardized, reproducible environment where AI agents learn to adapt UI layouts dynamically based on user behavior.

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2. Objective

Build an OpenEnv-compliant environment that simulates user interaction with UI layouts and enables agents to optimize for:

• Completion rate
• User satisfaction

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3. Success Metrics

• Deterministic grader score (0.0–1.0)
• Reproducible baseline results (±1% variance)
• Increasing reward trend across steps
• OpenEnv validation passes

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4. Tech Stack (Required)

Core Language

• Python 3.10+

Backend & Environment

• Pydantic (typed models)
• FastAPI (optional)

AI / Agent

• OpenAI API (baseline agent)

Simulation & Utilities

• NumPy
• random (seeded)

Visualization

• Streamlit / simple HTML renderer (for layout visualization)

Deployment

• Docker
• Hugging Face Spaces

Config

• YAML (openenv.yaml)

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5. System Design

5.1 Observation Schema

class Layout(BaseModel):
    button_size: float  # 0.5–2.0 (continuous in hard task)
    form_length: int    # 1–10
    steps: int          # 1–5

class Observation(BaseModel):
    device: Literal['mobile','desktop']
    layout: Layout
    progress: float
    last_action: str | None

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5.2 Action Schema

class Action(BaseModel):
    type: Literal[
        'increase_button',
        'decrease_form',
        'increase_steps',
        'decrease_steps',
        'reorder_sections',
        'set_button_size',  # continuous action (hard task)
        'noop'
    ]
    value: float | None

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5.3 Hidden State

• user_type ∈ {impatient, careful, new}
• tolerance threshold
• trust threshold

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6. User Simulation

Deterministic Rules

User Type	Condition	Outcome
impatient	steps > 3	drop
impatient	form_length > 5	drop
careful	form_length < 3	distrust
new_user	steps < 2	distrust

Probabilistic Layer

if outcome == "continue":
    if random(seed).random() < 0.1:
        return "drop"

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7. Reward Function

Let:

• C = completion
• P = progress
• D = drop

R = 0.5*C + 0.3*P - 0.4*D

Shaping:

• optimal button_size range (0.9–1.3) → +0.1
• steps ≤ 2 → +0.1
• form_length > 6 → -0.2
• repeated noop → -0.3

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8. Episode Lifecycle

• max_steps = 10 (default)
• extended mode: 20+ steps (scalability test)

Termination:

• complete
• drop
• max steps reached

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9. Tasks

Easy

• discrete actions only
• known user type

Medium

• mixed users
• stochastic transitions

Hard

• hidden user type
• continuous action (button_size tuning)
• conflicting objectives
• noisy feedback

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10. Grader

Run N=50 episodes

Metrics:

• completion_rate
• avg_reward

Score = 0.7 * completion_rate + 0.3 * avg_reward

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11. Benchmarking & Leaderboard

Include:

• Random policy baseline
• Heuristic rule-based baseline
• LLM-based baseline

Metrics:

• score
• avg_reward
• episodes-to-convergence

Leaderboard displayed in README / UI

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12. Visualization (WOW Factor)

• Render layout using Streamlit or HTML

• Show:

  • button size visually
  • number of form fields
  • step flow

• Integrate into HF Space UI

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13. Environment API

def reset() -> Observation

def step(action: Action) -> tuple[Observation, float, bool, dict]

def state() -> Observation

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14. openenv.yaml

name: ui_optimizer_env
version: 1.0

actions:
  - increase_button
  - decrease_form
  - increase_steps
  - decrease_steps
  - reorder_sections
  - set_button_size
  - noop

observations:
  device: string
  layout: object
  progress: float

tasks:
  - easy
  - medium
  - hard

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15. Baseline Agent

• deterministic
• temperature = 0
• fixed seeds

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16. Scalability Tests

• extended episode length (20+ steps)
• batch simulation (multiple users)
• stress test reward stability

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17. Non-Functional Requirements

• Dockerized
• HF Space deployable
• openenv validate passes
• reproducible outputs

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18. Edge Cases

• infinite loops → penalty
• invalid actions → ignore + penalty
• conflicting actions → last action wins

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19. Risks & Mitigation

Risk	Mitigation
weak simulation	hybrid rules + randomness
instability	fixed seeds
trivial agent success	stronger hard task

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20. Deliverables

• environment code
• tasks + grader
• baselines
• leaderboard
• visualization UI
• Dockerfile
• HF deployment
• README

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FINAL STATUS

✔ Fully optimized for hackathon scoring ✔ High novelty + strong evaluation ✔ Ready for implementation