Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
A survey on neural-symbolic learning systems
D Yu, B Yang, D Liu, H Wang, S Pan - Neural Networks, 2023 - Elsevier
In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …
superior perception intelligence. However, they have been found to lack effective reasoning …
A survey on interpretable reinforcement learning
Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …
for sequential decision-making problems, it is still not mature enough for high-stake domains …
The utility of explainable ai in ad hoc human-machine teaming
R Paleja, M Ghuy… - Advances in neural …, 2021 - proceedings.neurips.cc
Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to
enable humans to gain insight into the decision-making of machine learning models …
enable humans to gain insight into the decision-making of machine learning models …
Optimization methods for interpretable differentiable decision trees applied to reinforcement learning
Decision trees are ubiquitous in machine learning for their ease of use and interpretability.
Yet, these models are not typically employed in reinforcement learning as they cannot be …
Yet, these models are not typically employed in reinforcement learning as they cannot be …
A survey on explainable reinforcement learning: Concepts, algorithms, challenges
Towards resource-efficient edge AI: From federated learning to semi-supervised model personalization
A central question in edge intelligence is “how can an edge device learn its local model with
limited data and constrained computing capacity?” In this study, we explore the approach …
limited data and constrained computing capacity?” In this study, we explore the approach …