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IgnitionAI — Roadmap

Vision: The ML-Agents of the JavaScript creative ecosystem. Train RL agents in the browser, deploy anywhere via ONNX.

Positioning: Outil technique (comme Three.js), pas produit SaaS. Open-source first. Monétisation via services complémentaires plus tard.

Done

  • Core: TrainingEnv/InferenceEnv interfaces, auto-config, IgnitionEnv with train/infer/stop/setSpeed
  • Algos: DQN, PPO, Q-Table — with greedy mode for inference
  • Infrastructure: pnpm monorepo, Zod validation, CI/CD, ignitionai umbrella package
  • ONNX: OnnxAgent, TF.js→ONNX exporter, HF Hub loader
  • Storage: HuggingFace Hub provider
  • Environments: @ignitionai/environments — GridWorld, CartPole, MountainCar
  • Demos 2D: GridWorld, CartPole, MountainCar
  • Demos 3D: CartPole 3D, Car Circuit (dense progress reward), Drone Navigation (rigid-body physics hero demo)
  • 184+ tests passing

Phase 1 — Public Launch Prep ✅

Everything needed to post "Show HN" without getting roasted. All done.

1.1 Landing page ✅

  • Single-page site under packages/web (Next.js 16 + Tailwind 4)
  • Hero: install command + "Train your first agent" messaging
  • 7-line code snippet in Quickstart section
  • 6-demo grid (GridWorld, CartPole, MountainCar, CartPole 3D, Car Circuit, Drone Navigation)
  • IgnitionAI brand: flame GIF logo, indigo palette, custom SVG feature diagrams
  • Real package install command (@ignitionai/core + @ignitionai/backend-tfjs + @ignitionai/environments)
  • "Recent updates" section reading CHANGELOG.md at build time
  • Deployed on Vercel at ignitionai.dev

1.2 Documentation site ✅

  • Docs at /docs via Nextra 4, same Next.js deployment (single Vercel build)
  • Introduction + Quickstart (7-line CartPole, imports from @ignitionai/environments)
  • Verbose algorithm pages (DQN, PPO, Q-Table) with source-cited defaults and failure-mode recipes
  • "How it works" — one page per backend package with annotated source walkthroughs
  • React Three Fiber page: why R3F-first + full training-loop/render-loop split
  • 7 tutorials: GridWorld, CartPole observations, MountainCar reward shaping, CartPole 3D, Car Circuit, ONNX → Unity, Drone Navigation (physics + "IgnitionAI philosophy" section)
  • Auto-generated sidebar via _meta.js contracts, dark-mode matches landing
  • 26 routes built statically (landing, /changelog, /docs/*, 6 demo SPAs)

1.3 README + branding

  • README rewritten for v0.1 with current API + all demos
  • npm badges, license badge, test badge
  • Flame GIF logo (IgnitionAI brand asset)
  • Flame SVG favicon across all pages + demos (app/icon.svg + public/favicon.svg)
  • Root metadata: title template "— IgnitionAI", real description
  • Social card image for Twitter/OG
  • Hero GIF of Car Circuit or Drone demo in README

1.4 npm publish v0.1.0 ✅

  • All 5 packages published at 0.1.0: @ignitionai/core, @ignitionai/backend-tfjs, @ignitionai/backend-onnx, @ignitionai/storage, @ignitionai/environments
  • Docs updated post-publish: callouts removed, Quickstart uses @ignitionai/environments
  • Tag v0.1.0 on GitHub

1.5 Live demos ✅

  • 6 demos embedded as static routes under /demos/<slug>/ via prebuild pipeline
  • Each demo has ← IgnitionAI back link
  • Vite configs accept DEMO_BASE env var for per-route asset paths
  • Next.js rewrites resolve /demos/:slug/ to their index.html

1.6 Drone Navigation (hero demo) ✅

  • packages/demo-drone-navigation — quadcopter with hand-rolled rigid-body physics (gravity, drag, asymmetric thrust torque, semi-implicit Euler at 50 Hz)
  • 8 discrete actions (hover/forward/back/left/right/yaw), DQN-compatible
  • 13 normalized observations + dense reward (progress + anti-spin + capture bonus)
  • Tuned defaults: hiddenLayers: [64, 64], minEpsilon: 0.05, epsilonDecay: 0.998
  • Chase camera, target sphere, grid ground, HUD
  • Drone tutorial in docs with "Honest convergence expectations" section + "IgnitionAI philosophy" manifesto

1.7 Changelog ✅

  • /changelog page rendered from CHANGELOG.md at build time (zero-dep Markdown parser)
  • Landing "Recent updates" section showing latest release highlights
  • CHANGELOG.md updated with v0.2.0-dev covering everything since v0.1.0
  • Footer link to /changelog

1.8 Car Circuit reward fix ✅

  • Replaced +1 per on-track step with dense progress shaping (progressDelta × 300 + alignment + centerline)

Known framework bug (low priority)

  • backend-tfjs/src/defaults.ts:3-13 disagrees with agents/dqn.ts:43 on targetUpdateFrequency (100 vs 1000). Docs cite the runtime value (1000). Cleanup commit needed.

Phase 2 — Launch (next)

Get the framework in front of the right people.

  • Blog post: "How I built ML-Agents in JavaScript" (dev.to + personal blog)
  • Twitter thread: video of Drone Navigation learning to fly + 7-line code + the philosophy quote
  • Show HN: "IgnitionAI — Reinforcement Learning framework for JavaScript"
  • Reddit r/reinforcementlearning + r/javascript + r/threejs + r/reactjs
  • Product Hunt submission
  • Discord server: community + support channel
  • Social card image for Twitter/OG meta tags
  • Hero GIF in README (Drone Navigation or Car Circuit)
  • Track metrics: GitHub stars, npm downloads, demo page views

Phase 3 — Viral Demos (2 weeks)

Classic games everyone recognizes. These get shared.

  • Flappy Bird — AI masters Flappy in your browser
  • Snake — the classic, with growing snake visible
  • Dino Chrome — the offline Chrome game
  • Each demo: standalone page at /demos/<slug>/, deployed, shareable URL
  • Each demo: 60-sec video for Twitter

These are engineered for virality. "AI learns Flappy Bird in JavaScript" is a tweet magnet.

Phase 4 — Advanced Algorithms (2 weeks)

Continuous action spaces for real game AI.

  • SAC (Soft Actor-Critic) — continuous actions (steering angle, throttle)
  • A2C — lightweight alternative to PPO
  • Upgrade Drone Navigation + Car Circuit to continuous steering with SAC — smoother control
  • Benchmark: DQN vs PPO vs SAC on the same env

Phase 5 — Multi-Agent & Self-Play (3 weeks)

The next level of RL.

  • Multi-agent API: multiple agents in the same environment
  • Self-play: agent trains against past versions of itself
  • Demo: Pong — two agents learning to beat each other
  • Demo: Sumo — two agents wrestling in a circle

Phase 6 — Pre-Trained Models Hub (3 weeks)

HuggingFace for RL agents.

  • Upload API: agent.publish('username/model-name')
  • Download API: IgnitionEnv.loadAgent('username/model-name')
  • Gallery page: browse published agents
  • Top models: car racing, Snake champion, Flappy master, drone pilot
  • Leaderboard per environment

Phase 7 — DX Tooling (2 weeks)

Make it delightful to use.

  • create-ignitionai-app — starter template with env + demo
  • Web dashboard: live training visualization (start/stop/compare runs)
  • Replay viewer: step through past episodes for debugging
  • VS Code snippets extension

Phase 8 — Ecosystem Integrations (ongoing)

Meet developers where they are.

  • Three.js Journey integration: lesson on RL agents
  • Unity Sentis export guide: step-by-step tutorial ✅ (shipped as docs tutorial)
  • Unreal NNE export guide
  • Godot export (GDExtension via ONNX Runtime)
  • React Three Fiber starter kit
  • PlayCanvas integration

Phase 9 — Monetization (when ready)

Only after adoption. Don't put the cart before the horse.

Not before Phase 6 minimum. The framework must be widely used and loved before any commercial offering.

Possible models (ranked by feasibility):

  1. Enterprise support & consulting — game studios, XR companies, educational institutions
  2. Cloud training — offload heavy training to GPU cloud (like Replicate/Modal)
  3. Dashboard SaaS — hosted version of the web dashboard with team features
  4. Courses — paid tutorials on building game AI with IgnitionAI
  5. Dual license — MIT for open-source, commercial license for proprietary

Never: close-source the core framework, add paid algos, lock ONNX export behind a paywall.

Optional — Additional Demos

Build when time allows, for the gallery.

  • Drone navigation (target chasing with rigid-body physics)
  • Marble on tilting platform
  • Rocket landing (SpaceX vibe)
  • Robot arm (pick & place)
  • Atari Breakout clone
  • Bipedal walker

Guiding Principles

  1. Framework first, product second — the core library is the hero. Everything else supports it.
  2. Open-source forever — MIT license, no lock-in, no dark patterns.
  3. Creative devs first — Three.js / R3F users are the primary audience, not ML researchers.
  4. Browser-native — if it doesn't work in the browser, it doesn't ship.
  5. Zero config > full control — defaults must work. Advanced users get escape hatches.
  6. Ship > polish — iterate in public. Break things, learn fast.
  7. From brain to software — the friction between "I have an idea" and "I see it running" should be minutes, not weeks.

Built by @salim4n / @IgnitionAI