Inverse Game

Inverse game theory focuses on inferring the objectives or cost functions of agents within a game from observations of their actions, rather than assuming these objectives are known a priori. Current research emphasizes developing robust algorithms, often leveraging neural networks and Bayesian methods like variational autoencoders, to estimate these unknown parameters from potentially incomplete or noisy data, even in complex scenarios like multi-agent reinforcement learning and dynamic games with occluded agents. This field is crucial for advancing autonomous systems, human-AI collaboration, and other applications requiring understanding and predicting the behavior of interacting agents with unknown motivations. The development of efficient and accurate inverse game solvers is key to improving the safety and performance of these systems.