Unlocking the Power of AI: Combining Game Theory and Neural Networks

In this research paper, the authors explore the idea of combining game theory and neural networks to create powerful AI models. They highlight several examples of successful applications, including generative adversarial networks (GANs), robust reinforcement learning, adversarial training, multi-agent reinforcement learning in games, and even multiplayer games with natural language communication. However, they also acknowledge that these targets are excessively complex and require vast amounts of data to express and compute, even approximately. To overcome this challenge, the authors propose encoding the agents’ policies via a universal function approximator (such as a neural net) and training this architecture through iterative updates until convergence to the target equilibrium is achieved.
The authors begin by explaining that game-theoretic abstractions provide a clear and easy-to-understand target for AI models, but from a complexity-theoretic perspective, these targets are overly ambitious. They require enormous amounts of data to express and compute, even approximately. To address this challenge, the authors propose using a universal function approximator (UFA) to encode the agents’ policies, which allows them to train their models in a more manageable way.
The authors use an analogy to explain the UFA concept: "Imagine you want to cook a complex recipe but don’t have all the ingredients. You can either try to memorize the recipe or use a universal approximation function (UAF) that can approximate the missing ingredients based on the ones you already know." In this way, the UFA acts as a "proxy" for the complex target, allowing the AI model to learn and adapt in a more feasible manner.
The authors emphasize that their approach is not a panacea, but it can significantly improve the training process by reducing the complexity of the target. They provide examples of successful applications, such as GANs, robust reinforcement learning, and multi-agent games. However, they acknowledge that these models still face challenges related to scaling and convergence issues.
The authors conclude that their research provides a promising path forward for combining game theory and neural networks, but more work is needed to fully realize the potential of this approach. They suggest that by using UFAs to encode policies, AI models can learn and adapt in a more manageable way, making them more practical and efficient in complex domains.