  Temporal knowledge graphs (TKGs) model the temporal evolution of events and
have recently attracted increasing attention. Since TKGs are intrinsically
incomplete, it is necessary to reason out missing elements. Although existing
TKG reasoning methods have the ability to predict missing future events, they
fail to generate explicit reasoning paths and lack explainability. As
reinforcement learning (RL) for multi-hop reasoning on traditional knowledge
graphs starts showing superior explainability and performance in recent
advances, it has opened up opportunities for exploring RL techniques on TKG
reasoning. However, the performance of RL-based TKG reasoning methods is
limited due to: (1) lack of ability to capture temporal evolution and semantic
dependence jointly; (2) excessive reliance on manually designed rewards. To
overcome these challenges, we propose an adaptive reinforcement learning model
based on attention mechanism (DREAM) to predict missing elements in the future.
Specifically, the model contains two components: (1) a multi-faceted attention
representation learning method that captures semantic dependence and temporal
evolution jointly; (2) an adaptive RL framework that conducts multi-hop
reasoning by adaptively learning the reward functions. Experimental results
demonstrate DREAM outperforms state-of-the-art models on public dataset
