Hyperbolic Inverse Reinforcement Learning
Read the original on arXiv Machine Learning →Hyperbolic Inverse Reinforcement Learning (Hyperbolic IRL) introduces a reward function that depends on the Hyperbolic distance between feature vectors and a learned parameter vector, offering an alternative to traditional linear or neural network approaches. The method matches state‑of‑the‑art performance on standard benchmarks, performs especially well with limited data and in dense environments, and uncovers complex, non‑separable reward structures in human navigation tasks. Hyperbolic IRL thus enhances both predictive accuracy and interpretability of inferred reward functions.
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