CIP combines DirectLiNGAM-style causal masks for state-reward and action-reward links with counterfactual data augmentation and an empowerment objective to improve RL sample efficiency.
D.3.4 C OMPUTATION COST ANALYSIS We analyze the computational cost of the proposed framework
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Causal Information Prioritization for Efficient Reinforcement Learning
CIP combines DirectLiNGAM-style causal masks for state-reward and action-reward links with counterfactual data augmentation and an empowerment objective to improve RL sample efficiency.