The video explores the innovative concept of running the classic game Doom within a neural network, highlighting the challenges of non-deterministic gameplay generated in real-time, which leads to inconsistencies and visual artifacts. While this achievement demonstrates the potential for AI in interactive simulations, the current model is not yet robust enough for practical game development, serving primarily as a proof of concept rather than a fully functional game engine.
The video discusses the intriguing phenomenon of running the classic video game Doom on unconventional hardware, which has evolved into a cultural trend celebrated for its creativity and technological innovation. The speaker highlights that Doom has been successfully executed on various odd devices, including a camera and even a vape. Recently, researchers have taken this concept further by running Doom within a neural network in a non-deterministic manner, meaning that the game is generated in real-time without a fixed code or game engine. This approach allows for dynamic interaction with the game world, marking a significant advancement in AI’s ability to simulate and engage with environments.
The non-deterministic nature of this AI-generated Doom presents unique challenges, as it does not guarantee consistent outputs under the same conditions. The video explains that previous versions of Doom relied on deterministic machines, where the same inputs would yield the same results. In contrast, the AI model generates the game on-the-fly at 20 frames per second, leading to inconsistencies in gameplay and visual artifacts. The speaker emphasizes that while this achievement is groundbreaking, it is still far from being a fully functional game engine, as the AI struggles with maintaining coherent game states and interactions.
To create this AI-based game engine, researchers collected data by training AI agents to play Doom using reinforcement learning. These agents learned to mimic human-like interactions with the game, generating a dataset of action and frame pairs. The generative model used for this project is a modified version of the Stable Diffusion model, which typically generates images based on text prompts. In this case, the model takes in actions conditioned on previous frames to produce the next frame, but it faces challenges due to the limited context it can retain, which is only a few seconds of gameplay.
The video also delves into the technical aspects of the AI model’s training process, including the use of teacher forcing and noise augmentation to improve the model’s ability to handle its own mistakes. Despite these advancements, the speaker notes that the AI Doom engine is still not robust enough for practical use in game development. The reliance on a limited dataset and the inability to explore all possible interactions in the game lead to potential errors and inconsistencies during gameplay, which would be unacceptable in a commercial game engine.
Ultimately, while the achievement of running Doom in an AI’s brain is impressive and showcases the potential for interactive world simulation, the speaker concludes that we are still a long way from developing a fully functional AI-based game engine. The current model primarily replicates an existing game world rather than generating unique experiences from scratch. The video suggests that future advancements may require more comprehensive data collection and a foundational model capable of learning from a broader range of game designs, but for now, the project serves as a fascinating proof of concept for AI’s capabilities in gaming.