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Feature-rich agent-based simulation with real-time 3D visualization, reinforcement learning, IoT data ingestion, distributed scaling, and digital twin capabilities. Free and open-source (MIT).\n\u003c/p\u003e\n\n\u003e If you find this project useful, please consider giving it a ⭐ star — it helps more developers discover it!\n\n[![GitHub stars](https://img.shields.io/github/stars/rudra496/worldsim-ai?style=social)](https://github.com/rudra496/worldsim-ai/stargazers)\n[![GitHub forks](https://img.shields.io/github/forks/rudra496/worldsim-ai?style=social)](https://github.com/rudra496/worldsim-ai/network/members)\n[![GitHub watchers](https://img.shields.io/github/watchers/rudra496/worldsim-ai?style=social)](https://github.com/rudra496/worldsim-ai/watchers)\n[![GitHub discussions](https://img.shields.io/github/discussions/rudra496/worldsim-ai)](https://github.com/rudra496/worldsim-ai/discussions)\n\n[![Python 3.10+](https://img.shields.io/badge/Python-3.10+-3776AB.svg?style=flat\u0026logo=python\u0026logoColor=white)](https://python.org)\n[![FastAPI](https://img.shields.io/badge/FastAPI-0.104+-009688.svg?style=flat\u0026logo=fastapi\u0026logoColor=white)](https://fastapi.tiangolo.com)\n[![React](https://img.shields.io/badge/React-18-61DAFB.svg?style=flat\u0026logo=react\u0026logoColor=black)](https://reactjs.org)\n[![Three.js](https://img.shields.io/badge/Three.js-0.160-000000.svg?style=flat\u0026logo=three.js\u0026logoColor=white)](https://threejs.org)\n[![NumPy](https://img.shields.io/badge/NumPy-1.24+-013243.svg?style=flat\u0026logo=numpy\u0026logoColor=white)](https://numpy.org)\n[![PyTorch](https://img.shields.io/badge/PyTorch-Optional-EE4C2C.svg?style=flat\u0026logo=pytorch\u0026logoColor=white)](https://pytorch.org)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat)](LICENSE)\n[![Docker](https://img.shields.io/badge/Docker-Ready-2496ED.svg?style=flat\u0026logo=docker\u0026logoColor=white)](https://docker.com)\n[![PRs Welcome](https://img.shields.io/badge/PRs-Welcome-brightgreen.svg?style=flat)](CONTRIBUTING.md)\n[![v0.1.0](https://img.shields.io/badge/Release-v0.1.0-2ea44f.svg?style=flat)](https://github.com/rudra496/worldsim-ai/releases/tag/v0.1.0)\n\n\u003c/div\u003e\n\n---\n\n## ⚡ One Command Start\n\n```bash\ngit clone https://github.com/rudra496/worldsim-ai.git\ncd worldsim-ai\ndocker-compose up --build\n# 👉 Open http://localhost:3000\n```\n\n\u003cdetails\u003e\n\u003csummary\u003e📦 Alternative: Python-only (no frontend)\u003c/summary\u003e\n\n```bash\npip install -r requirements.txt\npython run_demo.py\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e🔧 Alternative: Full local development\u003c/summary\u003e\n\n```bash\n# Backend\npip install -r requirements.txt\nuvicorn worldsim.api.main:app --reload --port 8000\n\n# Frontend (separate terminal)\ncd frontend \u0026\u0026 npm install \u0026\u0026 npm start\n```\n\n\u003c/details\u003e\n\n---\n\n## ✨ Complete Feature Set\n\n### 🧬 Agent-Based Modeling\n- **4 agent types**: Vehicle, Human, Machine, EnergyUnit — each with customizable properties\n- **Rule-based \u0026 probabilistic behaviors** — deterministic or stochastic movement, production, consumption\n- **Multi-agent AI system** — autonomous Planner, Predictor, and Optimizer agents coordinate via AgentCoordinator\n\n### 🌐 Environment Modeling\n- **2D grid \u0026 graph world** representations with configurable dimensions\n- **8 zone types**: residential, industrial, commercial, road, park, power_plant, water_treatment, warehouse\n- **Resource management** — energy, water, materials, bandwidth with production/consumption tracking\n\n### 🧠 AI \u0026 Machine Learning\n- **PyTorch LSTM predictor** — deep time-series forecasting (NumPy fallback when PyTorch unavailable)\n- **Autoencoder anomaly detection** — unsupervised drift and outlier detection\n- **LP-based resource optimization** — scipy linear programming for allocation\n- **Priority scheduling** — greedy heuristic for task ordering\n- **Reinforcement learning** — Gymnasium-compatible environment with PPO/Q-learning agents\n- **Adaptive feedback loops** — simulation → AI prediction → optimization → correction cycle\n\n### 🎮 3D Visualization (Three.js)\n- **Interactive 3D world** via React Three Fiber — orbit, pan, zoom controls\n- **3D zone rendering** — translucent colored boxes with zone type labels\n- **Agent 3D objects** — vehicles (boxes), humans (spheres), machines (cylinders), energy (glowing spheres)\n- **Day/night cycle** — toggle between ambient lighting modes\n- **2D ↔ 3D switcher** — seamless view switching\n\n### 📡 Real-Time Data Ingestion\n- **MQTT source** — subscribe to IoT sensor topics with JSON parsing (requires `paho-mqtt`)\n- **File source** — CSV/JSON/JSONL ingestion with live tail support\n- **REST API source** — periodic polling of external endpoints\n- **Simulator source** — synthetic data with noise, drift, and failure injection\n- **Alert manager** — CRITICAL/WARNING/INFO with configurable callbacks\n\n### 🖥 Distributed Simulation\n- **Multi-node engine** — extends core engine for horizontal scaling\n- **Spatial partitioning** — grid-based agent distribution across nodes\n- **Load balancing** — threshold-based rebalancing with migration plans\n- **gRPC protocol** — dataclass-based message serialization (no protoc required)\n\n### 🏙 Digital Twin\n- **Live/replay/hybrid sync** modes — mirror real-world data or replay historical patterns\n- **GIS integration** — GeoJSON loading, coordinate transforms, geofencing with ray-casting\n- **Plugin system** — hot-reloadable plugins with ABC interface\n- **Built-in plugins** — LoggingPlugin, MetricsExportPlugin (Prometheus), SlackNotifyPlugin\n- **Plugin marketplace** — local catalog with built-in plugins (extensible to remote registries)\n- **REST + WebSocket connector** — push/pull state, bidirectional sync\n- **API key auth** with role-based access control\n- **Rate limiting** — token bucket algorithm\n\n### 📊 Dashboard \u0026 API\n- **React SPA** — dark theme, responsive, works standalone in demo mode\n- **Real-time WebSocket** — live simulation streaming to frontend\n- **9 API endpoints** — scenarios, simulations, results, metrics, health + WebSocket\n- **Metrics charts** — recharts for throughput, efficiency, stability, utilization\n- **CLI tool** — `worldsim run`, `list`, `demo`, `serve`\n\n### 🔬 Research Framework\n- ✅ **Deterministic mode** — Same seed → identical results (reproducible experiments)\n- ✅ **State snapshots** — Checkpoint and restore at any tick\n- ✅ **Event bus** — Full pub/sub for simulation events\n- ✅ **Multiple export formats** — JSON, CSV, structured text reports\n- ✅ **Config-driven** — YAML configs, no hardcoded values\n\n---\n\n## 🗺 Roadmap\n\n| Version | Status | Description |\n|---------|--------|-------------|\n| **v0.1** | ✅ Done | Core engine, 4 agent types, grid/graph worlds, AI prediction + optimization, 8 scenarios, REST + WebSocket API, React dashboard, Docker |\n| **v0.2** | ✅ Done | PyTorch LSTM predictor (NumPy fallback), autoencoder anomaly detection, Gymnasium RL env, PPO/Q-learning agents, multi-agent AI system, adaptive feedback loops |\n| **v0.3** | ✅ Done | Three.js 3D world (React Three Fiber), orbit controls, 3D zones/agents with glow, day/night cycle, 2D↔3D view switcher |\n| **v0.4** | ✅ Done | MQTT/File/API/Simulator data sources, ingestion pipeline, ring buffer, data transformer, alert manager |\n| **v0.5** | ✅ Done | Multi-node distributed engine, spatial partitioning, load balancing, gRPC protocol, message serialization |\n| **v1.0** | ✅ Done | Digital twin core (live/replay/hybrid), GIS integration, plugin system + marketplace, REST/WebSocket connector, API auth, rate limiting |\n\n\u003e 📖 See [ROADMAP.md](ROADMAP.md) for detailed feature checklists.\n\n---\n\n## 🏗 Architecture\n\n```mermaid\ngraph TB\n    subgraph Presentation [\"🖥️ Presentation Layer\"]\n        UI[\"React Dashboard\"]\n        Canvas[\"2D Canvas\"]\n        Three[\"3D View (Three.js)\"]\n        Charts[\"Metrics Charts\"]\n    end\n\n    subgraph API [\"📡 API Layer\"]\n        REST[\"REST Endpoints\"]\n        WS[\"WebSocket\"]\n        CLI[\"CLI Tool\"]\n    end\n\n    subgraph Core [\"⚙️ Simulation Core\"]\n        Engine[\"Simulation Engine\u003cbr/\u003eS(t+1) = F(S(t), A(t), E(t))\"]\n        Events[\"Event Bus\"]\n        State[\"State Manager\"]\n    end\n\n    subgraph Models [\"🧬 Modeling Layer\"]\n        Agents[\"Agent System (4 types)\"]\n        Env[\"Environment (Grid + Graph)\"]\n        Scenarios[\"Scenario Engine\"]\n    end\n\n    subgraph AI [\"🧠 Intelligence Layer\"]\n        Predict[\"LSTM Predictor\"]\n        Detect[\"Anomaly Detector\"]\n        Optimize[\"LP Optimizer\"]\n        RL[\"RL Agents (PPO)\"]\n        MAS[\"Multi-Agent System\"]\n        Feedback[\"Feedback Loops\"]\n    end\n\n    subgraph DataIO [\"📡 Data Layer\"]\n        MQTT_S[\"MQTT Source\"]\n        File_S[\"File Source\"]\n        API_S[\"API Source\"]\n        Sim_S[\"Simulator Source\"]\n    end\n\n    subgraph Twin [\"🏙 Digital Twin\"]\n        GIS[\"GIS / GeoJSON\"]\n        Plugins[\"Plugin System\"]\n        Market[\"Marketplace\"]\n        Connector[\"Twin Connector\"]\n    end\n\n    subgraph Dist [\"📊 Distributed\"]\n        Nodes[\"Simulation Nodes\"]\n        Partition[\"Spatial Partitioning\"]\n        GRPC[\"gRPC Protocol\"]\n    end\n\n    UI --\u003e REST\n    UI --\u003e WS\n    CLI --\u003e Engine\n    REST --\u003e Engine\n    WS --\u003e Engine\n    Engine --\u003e Agents\n    Engine --\u003e Env\n    Engine --\u003e Events\n    Engine --\u003e State\n    Scenarios --\u003e Engine\n    Predict --\u003e Engine\n    Detect --\u003e Engine\n    Optimize --\u003e Engine\n    RL --\u003e Engine\n    MAS --\u003e Engine\n    Feedback --\u003e Engine\n    MQTT_S --\u003e Engine\n    File_S --\u003e Engine\n    API_S --\u003e Engine\n    Sim_S --\u003e Engine\n    GIS --\u003e Engine\n    Plugins --\u003e Engine\n    Market --\u003e Plugins\n    Connector --\u003e Engine\n    Nodes --\u003e Engine\n    Partition --\u003e Nodes\n    GRPC --\u003e Nodes\n```\n\n### Formal Model\n\nEvery simulation step follows:\n\n```\nS(t+1) = F(S(t), A(t), E(t))\n```\n\n- **S(t)** — System state at time t (resources, metrics, counters)\n- **A(t)** — Agent actions at time t (movement, production, consumption)\n- **E(t)** — Environment factors at time t (zones, traffic, energy grid)\n- **F** — Transition function (combines all inputs → next state)\n\n---\n\n## 🎮 Demo Scenarios\n\n| # | Scenario | Description | Agents | Zones | Ticks |\n|---|----------|-------------|--------|-------|-------|\n| 🏙️ | **Smart City Traffic** | Urban traffic with vehicles \u0026 pedestrians across mixed-use city zones | 105 | 8 | 300 |\n| 🏭 | **Factory Optimization** | Production line with machines, workers, and energy constraints | 68 | 3 | 500 |\n| ⚡ | **Energy Balancing** | Multi-source energy grid with varying demand patterns | 85 | 8 | 400 |\n| 🌤️ | **Weather System** | Weather patterns with temperature, precipitation, and wind feedback | 90 | 13 | 600 |\n| 👥 | **Population Dynamics** | Population growth, migration, and resource competition | 133 | 10 | 500 |\n| 🔗 | **Supply Chain** | Multi-node supply chain with factories, warehouses, and retail | 110 | 10 | 400 |\n| 🌾 | **Smart Agriculture** | Precision agriculture with irrigation and automated harvesting | 65 | 10 | 500 |\n| 🚨 | **Emergency Failure** | System resilience testing under power outages and breakdowns | 76 | 6 | 400 |\n\n---\n\n## 📁 Project Structure\n\n```\nworldsim-ai/\n├── 🐍 worldsim/                    # Python simulation engine\n│   ├── core/                       # Engine, state, events\n│   ├── agents/                     # Agent-based modeling (4 types)\n│   ├── environment/                # World, zones, resources\n│   ├── ai/                         # Intelligence layer\n│   │   ├── predictor.py            # Linear regression, moving average\n│   │   ├── optimizer.py            # LP resource allocation\n│   │   ├── ml_models.py            # PyTorch LSTM, anomaly detection\n│   │   ├── reinforcement_learning.py # Gymnasium RL, PPO/Q-learning\n│   │   ├── multi_agent_system.py   # Planner, Predictor, Optimizer agents\n│   │   └── feedback.py             # Adaptive feedback loops\n│   ├── scenarios/                  # 4 demo scenario configs\n│   ├── api/                        # FastAPI REST + WebSocket\n│   ├── io/                         # Data ingestion (MQTT, File, API, Sim)\n│   ├── distributed/                # Multi-node simulation\n│   ├── twin/                       # Digital twin, GIS, plugins, marketplace\n│   ├── data/                       # Data pipeline \u0026 generation\n│   ├── utils/                      # Config, metrics, CLI\n│   └── cli.py                      # worldsim command-line tool\n│\n├── ⚛️ frontend/                    # React visualization\n│   ├── src/\n│   │   ├── components/             # Canvas, Charts, Controls, World3D\n│   │   ├── services/api.js         # Backend API client\n│   │   ├── utils/simulation.js     # Color maps, demo data\n│   │   └── App.js                  # Main dashboard (2D + 3D)\n│   ├── Dockerfile                  # Multi-stage build (node → nginx)\n│   └── nginx.conf                  # API proxy + SPA fallback\n│\n├── 📁 config/                      # YAML configurations\n├── 🧪 tests/                       # Test suite\n├── 📄 docs/                        # Architecture, simulation guide\n├── 🐳 docker-compose.yml           # One-command deploy (4 services)\n├── 🐳 Dockerfile                   # Backend image\n├── 📋 pyproject.toml               # Python packaging + optional deps\n└── 🎮 run_demo.py                  # Quick demo runner\n```\n\n---\n\n## 🛠 Tech Stack\n\n| Layer | Technology |\n|-------|-----------|\n| **Simulation** | Python 3.11+, NumPy, SciPy |\n| **AI/ML** | PyTorch (optional), Gymnasium (optional), stable-baselines3 (optional) |\n| **API** | FastAPI, Uvicorn, WebSockets |\n| **Frontend** | React 18, Three.js, React Three Fiber, recharts, HTML5 Canvas |\n| **IoT** | paho-mqtt (optional) |\n| **Distributed** | gRPC (optional), pickle/zlib |\n| **Digital Twin** | GeoJSON, shapely (optional) |\n| **Deployment** | Docker, Nginx |\n\n---\n\n## 📊 API Endpoints\n\n| Method | Endpoint | Description |\n|--------|----------|-------------|\n| `GET` | `/` | API health \u0026 info |\n| `GET` | `/scenarios` | List available scenarios |\n| `POST` | `/simulations/start` | Start a new simulation |\n| `GET` | `/simulations` | List all simulations |\n| `GET` | `/simulations/{id}` | Get simulation status |\n| `GET` | `/simulations/{id}/results` | Get simulation results |\n| `GET` | `/simulations/{id}/metrics` | Get aggregated metrics |\n| `GET` | `/health` | Health check |\n| `WS` | `/ws/simulations/{id}` | Real-time updates |\n\n---\n\n## 🤝 Contributing\n\nContributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n- 🆕 New agent types and behavior models\n- 🌍 New scenarios (epidemics, supply chain, climate...)\n- 📊 Visualization improvements\n- 🔌 New plugins for the marketplace\n- 📝 Documentation \u0026 tutorials\n- 🐛 Bug reports and feature requests\n\n---\n\n## 📄 License\n\n[MIT License](LICENSE) — free for personal and commercial use.\n\n---\n\n\u003cdiv align=\"center\"\u003e\n\n## 👨‍💻 Author\n\n**Rudra Sarker**\n\n[![GitHub](https://img.shields.io/badge/GitHub-rudra496-181717?style=flat\u0026logo=github)](https://github.com/rudra496)\n[![Twitter/X](https://img.shields.io/badge/X-@Rudra496-000000?style=flat\u0026logo=x)](https://twitter.com/Rudra496)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-Rudra_Sarker-0A66C2?style=flat\u0026logo=linkedin)](https://www.linkedin.com/in/rudrasarker)\n[![Facebook](https://img.shields.io/badge/Facebook-Rudra_Sarker-1877F2?style=flat\u0026logo=facebook)](https://www.facebook.com/rudrasarker130)\n[![Website](https://img.shields.io/badge/Website-rudra496.github.io-1DA1F2?style=flat\u0026logo=firefox)](https://rudra496.github.io/site)\n[![ResearchGate](https://img.shields.io/badge/ResearchGate-Rudra_Sarker-00CCBB?style=flat\u0026logo=researchgate)](https://www.researchgate.net/profile/Rudra-Sarker-3)\n[![SUST](https://img.shields.io/badge/SUST-Student-6C63FF?style=flat)](https://www.sust.edu)\n\n\u003cbr\u003e\n\n\u003ci\u003e🚀 Open Source Developer | AI tools, code analysis \u0026 developer education\u003c/i\u003e\n\u003cbr\u003e\n\u003ci\u003eCreator of TermMind, CodeVista, DevRoadmaps \u0026 SignLanguage Dataset Hub\u003c/i\u003e\n\n\u003cbr\u003e\u003cbr\u003e\n\n*Model the world. Optimize the future. Open source forever.* 🌍\n\n\u003c/div\u003e\n\n## Connect\n\n- [![GitHub](https://img.shields.io/badge/GitHub-rudra496-181717?logo=github)](https://github.com/rudra496)\n- [![LinkedIn](https://img.shields.io/badge/LinkedIn-rudrasarker-0A66C2?logo=linkedin)](https://www.linkedin.com/in/rudrasarker)\n- [![X/Twitter](https://img.shields.io/badge/X-@Rudra496-000000?logo=x)](https://x.com/Rudra496)\n- [![YouTube](https://img.shields.io/badge/YouTube-@rudrasarker9732-FF0000?logo=youtube)](https://youtube.com/@rudrasarker9732)\n- [![Dev.to](https://img.shields.io/badge/Dev.to-rudra__sarker-000000?logo=devdotto)](https://dev.to/rudra_sarker)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frudra496%2Fworldsim-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frudra496%2Fworldsim-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frudra496%2Fworldsim-ai/lists"}