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Full-Text Articles in Databases and Information Systems
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Research Collection School Of Computing and Information Systems
Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …
A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan
A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with.