> For the complete documentation index, see [llms.txt](https://docs.nrnagents.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nrnagents.ai/getting-started/model-initialization.md).

# Model Initialization

### Demo

In order to test out the SDK, users can create a demo agent that can start taking actions in their game! The purpose of the demo agent is to test out the entire flow of feeding in a state, taking actions, collecting data, and executing the actions in-game.

{% tabs %}
{% tab title="Javascript" %}

```javascript
const { AgentFactory } = require('nrn-agents');

const modelData = {
  config: {
    modelType: "neural-network",         // Type of model architecture
    inputDim: 5,                         // Number of features for the state
    neurons: [12, 6],                    // Number of neurons in each layer
    actionOrder: ["up", "down", "idle"]  // Order of action outputs
  }
}

const agent = AgentFactory.createDemoAgent(modelData)
```

{% endtab %}

{% tab title="C# - Unity" %}

```csharp
using NrnAgents.Agents;
using NrnAgents.MachineLearning;

ModelData modelData = new ()
{
    Config = new ()
    {
        ModelType = "neural-network",
        NFeatures = 5,
        Neurons = new List<int> () { 12, 6 },
        ActionMetadata = MetadataCreation.CreateActionMetadata(
            new AgentActions () { 
                ActionOrder = new ()  { "Up", "Down", "Idle" } 
            }
        )        
    }
};

DemoAgent Agent = AgentFactory.CreateDemoAgent(modelData);
```

{% endtab %}
{% endtabs %}

{% hint style="info" %}
Demo agents do not have the ability to learn
{% endhint %}

### Production

When initializing a model in-game, we need to use the following inputs:

* `architectureId`: A model architecture that has been registered
* `userId`: Unique identifier for a specific player
* `slotIdx`: Which model to use since each player may have multiple model slots

If NRN detects that there is already a model loaded for that user, it will load the trained model. In order to create a new randomly initialized model, developers can either input an unused `slotIdx` when creating the agent or call the `reset` method (currently only available in Javascript).

{% tabs %}
{% tab title="Javascript" %}

```javascript
const { AgentFactory } = require('nrn-agents');

const agent= AgentFactory.createAgent(
    "my-first-model", // architecture id
    "user-id",        // user id
    0,                // slot idx (default 0 if not provided)
)
await agent.initialize()

agent.reset() // To reset the agent
```

{% endtab %}

{% tab title="C# - Unity" %}

```csharp
using NrnAgents.Agents;

IAgentRL Agent = AgentFactory.CreateAgent<IAgentRL>(
    "reinforcement",  // learning algorithm type
    "my-first-model", // architecture id
    "user-id",        // user id
    0,                // slot idx
);
```

{% endtab %}
{% endtabs %}