adaptive_gamification 0.0.1

Adaptive gamification engine powered by reinforcement learning for dynamic difficulty adjustment.

Changelog

All notable changes to adaptive_gamification will be documented in this file.

The format is based on Keep a Changelog (https://keepachangelog.com/en/1.1.0/), and this project follows Semantic Versioning (https://semver.org/).

Unreleased

Added

• None.

Changed

• None.

Deprecated

• None.

Removed

• None.

Fixed

• None.

Security

• None.

0.0.1 - 2026-03-23

Initial public release of the package.

Added

• Core API
  • AdaptiveEngine with:
    • initFromAsset({ required String policyAssetPath, AssetBundle? bundle })
    • initFromString(String jsonString)
  • AdaptiveEngine.decide(UserState) returning an AdaptiveDecision.
• Models
  • UserState (difficulty index, accuracy, response time, correct streak).
  • AdaptiveDecision (next difficulty label and a human-readable reason).
• Policy loading & indexing
  • PolicyLoader to load and index an RL policy exported as JSON.
  • Deterministic lookup by a discretized RL-state key (fixed 2 decimals).
• RL state mapping utilities
  • StateMapper.toRlState() mapping app telemetry → RL state (eng, mot, flow, perf).
  • Grid discretization aligned with a 0.25 state resolution.
  • Stable key formatting to prevent lookup mismatches.
• Fallback behavior
  • Deterministic fallback decision logic when the exact policy entry is missing.