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A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control
Designing controllers to achieve natural motor capabilities for multi-joint robots is a significant challenge.
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely unexplored.
Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis
In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task.
Concept drift-tolerant transfer learning in dynamic environments.
Then, a domain-wise weighted ensemble is introduced to combine the source and target models to select useful knowledge of each domain.
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments
Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments.
