docs: comprehensive roadmap — 9 phases from launch to monetization

Phase 1: Launch prep (landing + docs + npm publish)
Phase 2: Public launch (HN, Reddit, Twitter, Product Hunt)
Phase 3: Viral demos (Flappy Bird, Snake, Dino Chrome)
Phase 4: Advanced algos (SAC, A2C, continuous actions)
Phase 5: Multi-agent + self-play (Pong, Sumo)
Phase 6: Pre-trained models hub (HuggingFace for RL)
Phase 7: DX tooling (starter, dashboard, replay)
Phase 8: Ecosystem integrations (Unity, Unreal, Godot, R3F)
Phase 9: Monetization (only after adoption)

Guiding principles: framework first, open-source forever, creative devs first.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: salim4n

roadmap.md

@@ -1,67 +1,182 @@
 # 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 (`env.train('dqn')`), IgnitionEnv with train/infer/stop
-- **Algos**: DQN, PPO, Q-Table — all with greedy mode for inference
-- **Infrastructure**: pnpm monorepo, Zod validation, CI/CD (GitHub Actions), `ignitionai` umbrella package
+- **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 (save/load/list/delete)
+- **Storage**: HuggingFace Hub provider
 - **Environments**: `@ignitionai/environments` — GridWorld, CartPole, MountainCar
-- **Demos 2D**: GridWorld, CartPole, MountainCar — train + inference mode, live reward chart
-- **Demo 3D**: CartPole 3D (R3F) — metallic cart, pole, rail, professional lighting
-- **184+ tests** passing across all packages
+- **Demos 2D**: GridWorld, CartPole, MountainCar
+- **Demos 3D**: CartPole 3D, Car on Circuit (chase cam, HUD, minimap, trail, speed slider)
+- **184+ tests** passing
 
 ---
 
-## Phase 1 — Wow Demo: Car on Circuit (IN PROGRESS)
+## Phase 1 — Public Launch Prep (2 weeks)
+
+> Everything needed to post "Show HN" without getting roasted.
+
+### 1.1 Landing page
+- [ ] Single-page site at `ignitionai.dev` (or similar domain)
+- [ ] Hero: Car on Circuit demo embedded live
+- [ ] 7-line code snippet with copy button
+- [ ] "Install" command block
+- [ ] 3 sub-demos linked (GridWorld, CartPole, MountainCar)
+- [ ] Deploy on Vercel
+
+### 1.2 Documentation site
+- [ ] Docs at `ignitionai.dev/docs` (Vitepress or Astro Starlight)
+- [ ] Getting Started (5 min tutorial)
+- [ ] `TrainingEnv` API reference
+- [ ] Algorithm guide (when to use DQN vs PPO vs Q-Table)
+- [ ] ONNX export + Unity/Unreal deployment guide
+- [ ] Architecture diagram
+
+### 1.3 README + branding
+- [ ] README with hero GIF of Car Circuit demo
+- [ ] npm badges, license badge, build status
+- [ ] Logo (simple, 5 min in Figma)
+- [ ] Social card image for Twitter/OG
+
+### 1.4 npm publish v0.1.0
+- [ ] Publish all 6 packages via `scripts/publish.sh`
+- [ ] Verify install in a fresh project
+- [ ] Tag v0.1.0 in git
 
-> A 3D car learns to drive on a circuit. The hero demo.
+---
 
-- [ ] Car on oval circuit with R3F + 3D model (.glb)
-- [ ] Discrete actions: steer left, straight, steer right
-- [ ] Observation: car position, angle, distance to track edges, velocity
-- [ ] Agent learns to stay on track and complete laps
-- [ ] Train → Inference toggle — car drives perfectly after training
-- [ ] Deploy on Vercel as shareable URL
+## Phase 2 — Launch (1 week)
 
-## Phase 2 — Landing Page & Docs
+> Get the framework in front of the right people.
 
-> Convert visitors into users.
+- [ ] **Blog post**: "How I built ML-Agents in JavaScript" (dev.to + personal blog)
+- [ ] **Twitter thread**: video of Car Circuit learning to drive + 10-line code
+- [ ] **Show HN**: "IgnitionAI — Reinforcement Learning framework for JavaScript"
+- [ ] **Reddit r/reinforcementlearning + r/javascript**
+- [ ] **Product Hunt** submission
+- [ ] **Discord server**: community + support channel
+- [ ] Track metrics: GitHub stars, npm downloads, demo page views
 
-- [ ] Landing page: hero demo embed, "10 lines of code" pitch, install command
-- [ ] Documentation site: Getting Started, TrainingEnv API, algorithm guide, ONNX export
-- [ ] README updated with badges, quickstart pointing to docs
+---
 
-## Phase 3 — Advanced Algorithms
+## 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, 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 steering angle, throttle
-- [ ] Upgrade car demo to continuous actions with SAC
-- [ ] A2C — lightweight alternative to PPO
+- [ ] **SAC** (Soft Actor-Critic) — continuous actions (steering angle, throttle)
+- [ ] **A2C** — lightweight alternative to PPO
+- [ ] Upgrade Car Circuit to continuous steering with SAC — smoother driving
+- [ ] Benchmark: DQN vs PPO vs SAC on the same env
+
+---
+
+## Phase 5 — Multi-Agent & Self-Play (3 weeks)
+
+> The next level of RL.
 
-## Phase 4 — Ecosystem & Growth
+- [ ] **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
 
-> Scale adoption.
+---
+
+## Phase 6 — Pre-Trained Models Hub (3 weeks)
+
+> HuggingFace for RL agents.
 
-- [ ] npm publish v0.1.0 (all packages + umbrella)
-- [ ] Example gallery: Three.js, R3F, vanilla canvas, Node.js headless
-- [ ] ONNX deployment guides: Unity (Sentis), Unreal (NNE)
-- [ ] `create-ignitionai-app` starter template
-- [ ] Blog post / Twitter launch
+- [ ] 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
+- [ ] Leaderboard per environment
 
 ---
 
-## Optional — Showcase Demos
+## 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
+- [ ] **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 hover (thrust balance)
+- [ ] Marble on tilting platform
+- [ ] Rocket landing (SpaceX vibe)
+- [ ] Robot arm (pick & place)
+- [ ] Atari Breakout clone
+
+---
 
-> Additional 3D demos for the gallery. Build when time allows.
+## Guiding Principles
 
-- [ ] **Drone hover** — 3D drone learns to stabilize mid-air (thrust 4 directions)
-- [ ] **Marble on tilting platform** — tilt to guide a ball to the target
-- [ ] **Rocket landing** — SpaceX-style inverted pendulum in 3D (thrust 4 directions)
-- [ ] **Robot arm** — pick & place with joint rotations
-- [ ] **Snake 3D** — classic snake game rendered in R3F
+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.
 
 ---