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Found 2 papers, 2 papers with code
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Inspired by this, we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner.
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Also, they are believed to be arranged hierarchically, allowing for an efficient representation of complex long-horizon experiences.
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