2023
DOI: 10.26599/tst.2022.9010063
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AInvR: Adaptive Learning Rewards for Knowledge Graph Reasoning Using Agent Trajectories

Hao Zhang,
Guoming Lu,
Ke Qin
et al.

Abstract: Multi-hop reasoning for incomplete Knowledge Graphs (KGs) demonstrates excellent interpretability with decent performance. Reinforcement Learning (RL) based approaches formulate multi-hop reasoning as a typical sequential decision problem. An intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance unstable. Current mainstream methods apply heuristic reward functions to counter this challenge.However, the inaccurate rewards caused by heuristic functions guide the ag… Show more

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