IgnitionAIIgnitionAI
Changelog

What we've shipped

Every release, with its full list of changes. Source of truth is CHANGELOG.md on GitHub.

v0.2.0-dev

— Landing, Docs, and Physics DemoApril 15, 2026

Landing & site

  • Public site live at [ignitionai.dev](https://ignitionai.dev)
  • Single Next.js deployment serving the landing, the docs, and the demos
  • Brand: animated flame GIF logo, indigo palette, 3 custom inline SVG feature diagrams (ONNX pipeline, algorithm cards, R3F integration)
  • Flame favicon across every page (`app/icon.svg`) and every demo

Documentation

  • 20-page Nextra documentation site under `/docs`
  • Introduction + Quickstart (7-line CartPole example, real package imports)
  • Verbose algorithm pages for DQN, PPO, and Q-Table with source-cited defaults and failure-mode recipes
  • One "How it works" page per backend package (`core`, `backend-tfjs`, `backend-onnx`, `storage`) with annotated source walkthroughs
  • React Three Fiber page explaining the training-loop / render-loop split
  • 7 step-by-step tutorials: GridWorld, CartPole observations, MountainCar reward shaping, CartPole 3D, Car Circuit, ONNX → Unity, Drone Navigation

Demos

  • 5 existing demos embedded as static routes under `/demos/<slug>/`: GridWorld, CartPole, MountainCar, CartPole 3D, Car Circuit
  • `← IgnitionAI` back link in every demo header
  • Prebuild pipeline that builds the library packages and each Vite demo with the right base path before the Next.js build

Drone Navigation (new hero demo)

  • `@ignitionai/demo-drone-navigation` — a quadcopter that learns to fly to moving target points
  • Hand-rolled rigid-body physics: gravity, drag, torque from asymmetric thrust, semi-implicit Euler integration at 50 Hz
  • 8-action discretization (hover / forward / back / left / right / yaw) so DQN can handle 6-DOF flight
  • Reward shaping: distance + progress delta + anti-spin + capture bonus + crash penalty
  • Tuned defaults: `hiddenLayers: [64, 64]`, `minEpsilon: 0.05`, `epsilonDecay: 0.998` — visible hovers around episode 200–400

Car Circuit reward fix

  • Replaced the `+1 per on-track step` reward with dense progress shaping (`progressDelta × 300` + alignment + centerline). The old reward treated "spinning in place" and "making laps" as equally valuable.

v0.1.0

— First Public ReleaseApril 11, 2026

@ignitionai/core

  • `TrainingEnv` interface — implement 5 methods + actions to define your environment
  • `InferenceEnv` interface — minimal observe + step for production deployment
  • `IgnitionEnv` — training loop with `train()`, `infer()`, `stop()`, `reset()`
  • Auto-config: deduces inputSize/actionSize from your environment
  • Algorithm switching: `env.train('dqn')`, `env.train('ppo')`, `env.train('qtable')`
  • Greedy mode for inference: `getAction(obs, greedy=true)`

@ignitionai/backend-tfjs

  • DQN — Deep Q-Network with replay buffer, target network, epsilon-greedy
  • PPO — Proximal Policy Optimization with Actor-Critic, GAE-lambda, entropy bonus
  • Q-Table — Tabular Q-Learning with mixed-radix state discretization
  • `IgnitionEnvTFJS` — IgnitionEnv with TF.js agent factories baked in
  • Sensible defaults per algorithm — zero config required
  • Configurable TF.js backend selector (WebGPU > WebGL > WASM > CPU)

@ignitionai/backend-onnx

  • `OnnxAgent` — inference-only agent for ONNX Runtime
  • TF.js → ONNX exporter with Python conversion script
  • HuggingFace Hub loader for .onnx models

@ignitionai/storage

  • `ModelStorageProvider` interface (save/load/list/delete/exists)
  • HuggingFace Hub provider with retry and Zod config validation

@ignitionai/environments

  • `GridWorldEnv` — NxN grid pathfinding (discrete)
  • `CartPoleEnv` — Pole balancing with Euler physics (continuous state)
  • `MountainCarEnv` — Momentum-based hill climbing (sparse reward)
  • All implement `TrainingEnv` — plug and play with `IgnitionEnv`
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