Using hyperbolic geometry to parameterize reward functions in IRL can capture non-separable feature interactions better than standard approaches, especially when training data is limited or environments are dense.
This paper introduces Hyperbolic Inverse Reinforcement Learning, which infers reward functions from observed behavior using hyperbolic geometry instead of traditional linear or neural network approaches.