Machine Learning in Action: Creating Video Game Levels

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In Part 1, we learned that machine learning leverages generic algorithms to derive insights from data without writing specific code for every unique problem. In this installment, we will explore one of these algorithms as it generates video game levels that resemble those crafted by humans. We will develop a neural network, feed it existing Super Mario levels, and observe the cool creations that emerge!

Here is an example of one of the levels our algorithm can generate:

Much like in Part 1, this guide is accessible to anyone curious about machine learning, regardless of their starting point. Our aim is to foster interest in ML, even if it means simplifying complex concepts.

Making Smarter Guesses

In the first part, we devised a simple algorithm that estimated house values based on certain attributes. For example, given the data of a house, we derived an estimation function like this:

def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
    price = 0
    price += num_of_bedrooms * 0.123
    price += sqft * 0.41
    price += neighborhood * 0.57
    return price

The function derived a house’s value by multiplying its attributes with specific weights. This can be visualized simply:

However, this algorithm only captures simple relationships. If housing market variables interact in complex ways, like neighborhood effects differing with house size, we need a more sophisticated approach.

To enhance our estimates, we could apply our algorithm multiple times with varied weights that reflect different conditions. By combining these diverse estimates, we could achieve a more accurate final result.

What is a Neural Network?

When we chain together these diverse attempts to make predictions, we create what we call a neural network. Each node within the network receives inputs, applies weights, and computes an output value. This architecture allows us to evaluate complex functions.

Although many technical details (like feature scaling and activation functions) are omitted for brevity, the core principles can be summarized:

1. Simple Functions = Neurons: Each neuron is a basic function that processes inputs with weights.
2. Complex Models = Networks of Neurons: By chaining multiple neurons, we can represent functions that a single neuron cannot model.

Enhancing Neural Networks with Memory

The neural network we just discussed lacks memory — when provided with the same inputs, it delivers consistent outputs. This simplicity is useful for fixed-price estimations but insufficient for identifying temporal patterns across data.

Consider a scenario where you must predict the next letter someone will type. By utilizing accumulated knowledge of common sequences and previously typed letters, we can apply this information to enhance accuracy.

For instance, in the phrase, “Robert Cohn was once middleweight boxi,” predicting the next letter likely leads to the ‘n’ in “boxing”. Here, understanding the word sequences helps improve predictions substantially.

To effectively tackle this issue with a neural network, we must augment our model with state retention. Each time we query the network, we save intermediate results to utilize them in future predictions.

This concept defines Recurrent Neural Networks (RNNs), which maintain a form of memory, enabling them to refine projections based on recent inputs.

The Value of Predicting Letters

You might wonder what the purpose of predicting the next letter is. One practical application could be enhancing autocorrect features on mobile keyboards.

Imagine scaling this concept to continually predict characters. It opens up the potential for the algorithm to write full narratives. Using an RNN implementation developed by Andrej Karpathy, we can explore generating text akin to Hemingway’s style.

Using the text from The Sun Also Rises, we could initially observe the network struggle as it learns; however, with sufficient training, the generated content begins reflecting genuine structure and style.

Creating Mario Levels with Machine Learning

Fascinatingly, we can apply this learning model used to generate text to create Super Mario levels! In Super Mario Maker, users can design levels using classic elements like power-ups and enemies.

To train our algorithm for specific Mario levels, we can analyze outdoor levels from the original Super Mario Bros game, extracting patterns from the level design. Each game object can be represented as a character in a grid format, allowing the algorithm to learn effective configurations for different game elements.

After thorough training, the generated levels might begin to exhibit suitable structures, creating a valid representation of a Super Mario level.

Conclusion

The recurrent neural network techniques used in this project are the same as those employed by industry leaders for challenges in speech recognition and translation. However, the model’s effectiveness relies significantly on the amount and quality of data. Without access to ample training data, even advanced algorithms can produce only modest results.

As machine learning continues to evolve, the importance of quality datasets becomes increasingly paramount. With enough data, these models not only create compelling game levels but also address real-world challenges across various industries.

For further exploration of machine learning strategies and concepts, consider subscribing to relevant communities or courses. Engaging with these resources facilitates a deeper understanding of this transformative technology.

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Feel free to ask if you have additional needs or want any more images to clarify these concepts!