These problems compound as the complexity of the training environment increases and multiple AI agents are introduced. Furthermore, the relevant algorithms are complex and delicate to tune. Due to the limitations of many current learning algorithms, hundreds of thousands of rounds of gameplay are required, which is not possible without a sufficient supply of computational resources, e.g., high-performance computing platforms equipped with many CPUs, GPUs, or specialized hardware. ![]() Game research helps us build models for AI that can plan, reason, navigate, solve problems, collaborate, and communicate.ĭespite the many benefits of using games for training, it can be difficult for individuals to conduct AI research in a game environment. These attributes are helping facilitate research underway in Facebook’s AI Research lab (FAIR) to explore both short-term milestones like demonstrating the strength of AI in multiple complicated game environments, and long-term goals aimed at applying AI to real-world challenges. ![]() Within a game environment, the amount of labeled training data available for training AI models is nearly infinite, low-cost, replicable, and more easily obtained at a much higher rate than in real-world experiments. ![]() Games provide a useful environment for artificial intelligence research.
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