# 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 %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.nrnagents.ai/getting-started/model-initialization.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.