2026
Learning Interpretable Options by Identifying Reward Diffusion Bottlenecks in Reinforcement Learning
ICML 2026poster
Bottleneck states, which connect distinct regions of the state space, provide a principled and interpretable basis for constructing temporal abstractions in Hierarchical Reinforcement Learning (HRL). However, existing bottleneck identification methods primarily rely on topological analysis of the st…