… , and has the advantages of robust interpretability and flexibility and of representing the …
the occurrence rules of social events is still in its early stages[28][21]. This study used several …

… In this study, instead of identifying how similar the documents … policies in each five-year plan,
the impact of the policies on … , they do not ensure the interpretability of the topic output. For …

… text must be interpretable as coherent with … policies, so translators should use abridgement
or omission to reproduce these jargons in a natural and concise way. Based on the learning …

… to dealing with urban traffic signal control. Swarm intelligence … ment learning, have been
widely used in traffic signal control … Policy gradient methods for reinforcement learning with …

… as interpretable. … strategy of human understanding keeps concludes that work out positively
past specific capacity. [1] Therefore, interpretability is a central quality that machine learning …

… key road infor⁃ mation, which enhances the interpretability of … agent to learn a more
human-like driving strategy. Finally, a … design in deep QLearning network for traffic light control[C]//…

… Due to its good interpretability, the MLR has become one of the most commonly used … The
results of this study indicate that forest management policies such as Mountain Distribution to …

… learning, hybrid deep reinforce learning incorporating system operation rules, causal analysis,
and interpretable AI … In the context of power system dispatch and control, three examples …

… Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks.
… Guided cost learning: Deep inverse optimal control via policy optimization. In: Proceedings …

… based on interpretable performance: weakly interpretable end-… imitation learning, reinforcement
learning, and interpretability; … Efficient learning of safe driving policy via human-ai copilot …