Vivarium

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A high-performance evolutionary simulation powered by C# .NET 10 and MonoGame. Agents with recurrent neural network brains evolve complex survival strategies, exhibiting emergent behaviors like swarming, combat, and resource management.

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# Vivarium

**Vivarium** is a high-performance artificial life simulation written in **C# (.NET 10)** using the **MonoGame** framework.

The project simulates an ecosystem of autonomous agents ("Creatures") interacting within a dynamic grid-based world containing plants and structures.

[![Imgur](https://i.imgur.com/V1H9RqRm.png)](https://imgur.com/V1H9RqR) [![Imgur](https://i.imgur.com/oB2BjnHm.png)](https://imgur.com/oB2BjnH)

## 📖 Documentation

| Document | Description |
|----------|-------------|
| **[Home](https://kromtec.github.io/Vivarium/docs/)** | Introduction into the documentation
| **[User Manual](https://kromtec.github.io/Vivarium/docs/user-manual.html)** | Complete guide to understanding the simulation, neural networks, genetics, and ecosystem dynamics |
| **[Configuration Guide](https://kromtec.github.io/Vivarium/docs/configuration.html)** | Detailed configuration parameters and examples |
| **[API Documentation](https://kromtec.github.io/Vivarium/docs/api/Vivarium.html)** | Code reference generated from XML comments |

## 🧬 Key Features

* **Evolutionary Neural Networks:** Agents possess a genome encoding a Recurrent Neural Network (RNN), allowing for memory and complex decision-making.
* **Complex Phenotypes:** Genes determine physical traits (Strength, Speed, Perception, etc.) and neural wiring.
* **Dynamic Ecosystem:**
    * **Herbivores:** Graze on plants, efficient metabolism, high resilience.
    * **Carnivores:** Hunt other agents, high energy cost, aggressive.
    * **Omnivores:** Adaptable diet, balanced traits.
* **Emergent Behavior:** Watch as agents evolve strategies for hunting, grazing, swarming, and combat without hard-coded rules.
* **High Performance:** Optimized using `Span<T>` and Struct-based architecture to support thousands of agents at 60 FPS.
* **Interactive Camera:** Full 2D camera support with zoom and panning to observe the simulation at macro and micro levels.
* **Headless Mode:** Run simulations without graphics for rapid training and data collection.

[![Imgur](https://i.imgur.com/VdaAf4G.png)](https://imgur.com/VdaAf4Ghttps://imgur.com/VdaAf4G)

## 🎮 Controls

* **WASD / Arrow Keys:** Pan Camera
* **Scroll Wheel:** Zoom In/Out
* **Left Click:** Select Agent/Entity (View details in Inspector)
* **Right Click:** Deselect
* **Space:** Pause/Resume Simulation
* **.(Dot):** Step Forward One Tick (when paused)

![Imgur](https://i.imgur.com/8EuuIDM.png)

## 🚀 Running the Simulation

### Standard Mode (Visual)
Run the project normally through your IDE or terminal:
```bash
dotnet run --project src/Simulation/Vivarium.csproj -c Release
```
*Note: Using `Release` configuration significantly improves performance.*

### Headless Mode (Fast Simulation)
For rapid evolution or data gathering, run in headless mode. This disables rendering and runs the simulation as fast as the CPU allows.
```bash
dotnet run --project src/Simulation/Vivarium.csproj -c Release -- --headless --duration 36000
```
*   `--headless`: Enables headless mode.
*   `--duration <ticks>`: Sets the simulation duration (60 ticks = 1 second). Default is 3600 (1 minute).
*   `--seed <int>`: (Optional) Set a specific random seed for reproducibility.
*   `--config <path>`: (Optional) Load simulation configuration from a JSON file.

## ⚙️ Configuration System

The simulation uses a modular configuration system that allows balancing parameters to be tweaked without code changes.

### Configuration Files
Configuration can be loaded from JSON files using the `--config` argument:
```bash
dotnet run --project src/Simulation/Vivarium.csproj -c Release -- --config myconfig.json
```

### Configuration Categories

| Category | Mutability | Description |
|----------|------------|-------------|
| `WorldConfig` | Immutable | Grid size, entity pool sizes, seed |
| `AgentConfig` | Runtime | Agent balance (metabolism, reproduction, movement, combat) |
| `PlantConfig` | Runtime | Plant balance (photosynthesis, reproduction, aging) |
| `BrainAIConfig` | Runtime | Neural network and instinct parameters |
| `GeneticsConfig` | Partial | Mutation rate (mutable), genome structure (immutable) |

### Example Configuration
```json
{
  "world": {
    "gridHeight": 96,
    "gridWidth": 170,
    "cellSize": 13,
    "agentPoolSize": 906,
    "plantPoolSize": 2040,
    "structurePoolSize": 510,
    "seed": 64
  },
  "agent": {
    "reproductionOverheadPct": 0.30,
    "baseMetabolismRate": 0.01,
    "maturityAge": 600,
    "reproductionCooldownFrames": 600
  },
  "plant": {
    "photosynthesisRate": 0.50,
    "shrivelRate": 0.40,
    "maturityAge": 600
  },
  "brain": {
    "hiddenNeuronDecayFactor": 0.5,
    "feedingInstinctThreshold": 0.6,
    "reproductionInstinctThreshold": 0.9
  },
  "genetics": {
    "genomeLength": 512,
    "traitGeneCount": 14,
    "mutationRate": 0.001
  },
  "framesPerSecond": 60.0
}
```

## 🛠️ Tech Stack

* **Language:** C# 14 / .NET 10
* **Engine:** MonoGame (DesktopGL)
* **Architecture:** Entity Component System (ECS) inspired, struct-based arrays for memory efficiency.

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