C++ and Unreal Engine 5 Libraries for Crating Custom Deep Learning Models

Photo by Brecht Corbeel on Unsplash

It has been too long since I last wrote an article on Medium, the truth is, I’ve been focusing on learning a few methods on optimizing UE5 simulations with custom made deep learning models. And now’s the chance to share with you all!

Let’s go by simple topics, shall we?

The Power of C++ in Deep Learning

C++ has long been a staple in software development, renowned for its efficiency, speed, and low-level control. While high-level languages like Python are often preferred for their ease of use and rapid prototyping capabilities, C++ shines when it comes to performance-critical applications, such as deep learning. Leveraging C++ for deep learning projects offers several advantages:

1. C++ allows developers to optimize their code for maximum performance, making it well-suited for computationally intensive tasks like training deep neural networks.
2. With C++, developers have fine-grained control over memory management and hardware resources, enabling them to squeeze out every bit of performance from their algorithms.
3. Many existing libraries and frameworks for deep learning, such as TensorFlow and PyTorch, provide C++ APIs, allowing developers to seamlessly integrate deep learning functionality into their C++ applications.

Unreal Engine 5: A Game-Changing Platform

Unreal Engine 5, the latest iteration of Epic Games’ renowned game engine, has garnered attention not only in the gaming industry but also in other fields such as architecture, film, and simulation. With its cutting-edge graphics capabilities and robust toolset, UE5 offers a compelling platform for developing deep learning models:

1. UE5’s advanced rendering capabilities enable the creation of highly realistic virtual environments, making it ideal for simulating real-world scenarios for training and testing deep learning models.
2. Unreal Engine comes with built-in tools for AI development, including behavior trees, perception systems, and navigation meshes, facilitating the integration of deep learning algorithms into interactive applications.
3. UE5 is designed to leverage modern hardware efficiently, making it capable of handling large-scale simulations and complex AI tasks with ease.

Combining C++ and UE5 for Deep Learning

By harnessing the power of C++ and Unreal Engine 5 libraries, developers can create custom deep learning models with unprecedented realism and performance. Here’s how:

1. C++ APIs provided by popular deep learning frameworks such as TensorFlow and PyTorch allow developers to seamlessly integrate deep learning functionality into Unreal Engine projects. This enables the creation of AI-driven characters, interactive environments, and immersive experiences.
2. With UE5’s high-fidelity simulation capabilities, developers can train and evaluate deep learning models in real-time within virtual environments. This facilitates rapid prototyping and testing of AI algorithms without the need for physical hardware.
3. The integration of deep learning into Unreal Engine opens up new possibilities for cross-disciplinary collaboration between game developers, AI researchers, and domain experts. By working together, teams can leverage their respective expertise to create innovative solutions to complex problems.

But how to actually do it? Here’s a step by step guide with checkboxes to tick along the way.

Writing a deep learning model for Unreal Engine 5 (UE5) using C++ involves several steps, from setting up the development environment to integrating the model into your UE5 project. Below is a step-by-step guide to help you get started:

Step 1: Install Unreal Engine 5: Download and install Unreal Engine 5 from the official Epic Games website. Follow the installation instructions provided.

Step 2: Install Deep Learning Framework: Choose a deep learning framework that offers C++ APIs, such as TensorFlow or PyTorch. Install the framework and its dependencies on your development machine.

Step 3: Create a New Unreal Engine Project — Open Unreal Engine: Launch Unreal Engine 5 and create a new project. Choose the desired project template (e.g., Blank, First Person, Third Person) based on your requirements.

Step 4: Set Up Project Settings: Configure project settings such as project name, location, and target platform (e.g., Windows, macOS, iOS, Android).

Step 5: Implement the Deep Learning Model in C++ — Create a New C++ Class: In the Unreal Editor, navigate to the “File” menu and select “New C++ Class”. Choose the appropriate class template (e.g., Actor, Pawn, Character) depending on the context in which you want to use the deep learning model.

Step 6: Write the Model Code: Implement your deep learning model using the chosen deep learning framework’s C++ API. Define the model architecture, including layers, activations, and loss functions. You may also load pre-trained models if needed.

Step 7: Integrate Model with Unreal Engine: Write code to integrate the deep learning model with Unreal Engine. This may involve processing input data (e.g., images, sensor data) from the game environment, running inference on the model, and using the output for gameplay or visualization purposes.

Step 8: Compile and Test Your Code — Compile the Project: Build your Unreal Engine project to compile the C++ code and link it with the rest of the project assets.

Step 9: Give it a test!: Launch the Unreal Editor in “Play” mode to test your deep learning model within the game environment. Verify that the model behaves as expected and produces the desired results.

Hope this helps!

Application example: https://medium.com/deepgamingai/deep-learning-for-animating-player-movements-control-and-interactions-in-a-game-environment-aaa2ae62b6d0