Dear Editor,This letter addresses the enhancement of autonomous vehicles’(AVs)behavior control systems through the application of reinforcement learning(RL)techniques.It presents a novel approach to efficient knowled...Dear Editor,This letter addresses the enhancement of autonomous vehicles’(AVs)behavior control systems through the application of reinforcement learning(RL)techniques.It presents a novel approach to efficient knowledge-guided self-evolutionary intelligent decision-making by integrating human intervention as prior knowledge into the RL’s exploratory learning process.Specifically,we propose an innovative intervention-based reward shaping mechanism and develop a novel experience replay mechanism to augment the efficiency of leveraging guided knowledge within the framework of off-policy RL.The proposed methodology significantly enhances the performance of RL-based behavior control strategies in complex scenarios for AVs.Illustrative results indicate that,relative to existing state-of-theart methods,our approach yields superior learning efficiency and improved autonomous driving performance.展开更多
Tissue engineering is a branch of regenerative medicine that harnesses biomaterials and stem cells to utilize the body’s natural healing responses to regenerate tissue and organs.Skin components can be rebuilt by saf...Tissue engineering is a branch of regenerative medicine that harnesses biomaterials and stem cells to utilize the body’s natural healing responses to regenerate tissue and organs.Skin components can be rebuilt by safeguarding their structure and function with the help of advanced scaffold manufacturing techniques.It is important to combine medical concerns with the vast explosion of artificial intelligence concepts to preserve human life and improve health.Currently,machine learning can make reliable contributions to critical decision-making in a wide range of applications.Regression machine learning models rely on correlations,associations,and other relationships between a dependent variable and a group of features.The main objective of this research was to study the effects of applying machine learning techniques on the performance of nanoscaffolds.A regression tree,a random forest,AdaBoost,and a gradient boosting algorithm were applied to the dataset and clustering data.By comparing our proposed models with the relevant studies to verify each machine learning model’s optimal performance,the AdaBoost technique was shown to have the highest accuracy(98.58%,99.6%,98.51%,and 98.85%),with a mean absolute percentage error of 1.41%and an R^(2) value of 0.999,which indicates a strong correlation between the predicted and actual values for the whole dataset and all subgroups.展开更多
基金supported by the National Natural Science Foundation of China(62373224)by the Natural Science Foundation of Shandong Province(ZR2024JQ021).
文摘Dear Editor,This letter addresses the enhancement of autonomous vehicles’(AVs)behavior control systems through the application of reinforcement learning(RL)techniques.It presents a novel approach to efficient knowledge-guided self-evolutionary intelligent decision-making by integrating human intervention as prior knowledge into the RL’s exploratory learning process.Specifically,we propose an innovative intervention-based reward shaping mechanism and develop a novel experience replay mechanism to augment the efficiency of leveraging guided knowledge within the framework of off-policy RL.The proposed methodology significantly enhances the performance of RL-based behavior control strategies in complex scenarios for AVs.Illustrative results indicate that,relative to existing state-of-theart methods,our approach yields superior learning efficiency and improved autonomous driving performance.
文摘Tissue engineering is a branch of regenerative medicine that harnesses biomaterials and stem cells to utilize the body’s natural healing responses to regenerate tissue and organs.Skin components can be rebuilt by safeguarding their structure and function with the help of advanced scaffold manufacturing techniques.It is important to combine medical concerns with the vast explosion of artificial intelligence concepts to preserve human life and improve health.Currently,machine learning can make reliable contributions to critical decision-making in a wide range of applications.Regression machine learning models rely on correlations,associations,and other relationships between a dependent variable and a group of features.The main objective of this research was to study the effects of applying machine learning techniques on the performance of nanoscaffolds.A regression tree,a random forest,AdaBoost,and a gradient boosting algorithm were applied to the dataset and clustering data.By comparing our proposed models with the relevant studies to verify each machine learning model’s optimal performance,the AdaBoost technique was shown to have the highest accuracy(98.58%,99.6%,98.51%,and 98.85%),with a mean absolute percentage error of 1.41%and an R^(2) value of 0.999,which indicates a strong correlation between the predicted and actual values for the whole dataset and all subgroups.
