most citedPerformance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective

5 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2019

Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm

Nikki Lijing Kuang, Clement H. C. Leung

In the search and retrieval of multimedia objects, it is impractical to either manually or automatically extract the contents for indexing since most of the multimedia contents are…

cs.AI2019

Analysis of Evolutionary Behavior in Self-Learning Media Search Engines

Nikki Lijing Kuang, Clement H. C. Leung

The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a SelfLearning Search Engine architecture, the subtle nuances of human per…

cs.LG2019

Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation

Nikki Lijing Kuang, Clement H. C. Leung

Rewards and punishments in different forms are pervasive and present in a wide variety of decision-making scenarios. By observing the outcome of a sufficient number of repeated tri…

cs.LG20195 cited

Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective

Nikki Lijing Kuang, Clement H. C. Leung

In reinforcement learning, a decision needs to be made at some point as to whether it is worthwhile to carry on with the learning process or to terminate it. In many such situation…

cs.LG20194 cited

Stochastic Reinforcement Learning

Nikki Lijing Kuang, Clement H. C. Leung, Vienne W. K. Sung

In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation. Such s…