Cross Validation mechanism for an RL case. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Monte carlo test ranking from elitism phase. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Individual #5’s action ratio, position states. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

RSF Components of the best five individuals. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Open loop simulation. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Average wind test fitness. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Internal process of a policy gradient block. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Training process of a DDPG individual. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

PbGA search phases to find the best individuals. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Previous usages of DRL in solving PDG problems. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Internal process of a critic gradient block. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Best Individuals from the mapping phase. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX حسب Nina de Lacy (6559520)

"…In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. …"

Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX حسب Nina de Lacy (6559520)

"…In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. …"

The_Code_for_High_Order_Analytical_Continuation حسب Jian Ma (19747060)

"…The optimal order of the analytical continuation algorithm is contingent upon the noise level of gravity data. …"

Additional information on the sequence model. حسب Jérôme Tubiana (14559981)

Low-Order Scaling <i>G</i>₀<i>W</i>₀ by Pair Atomic Density Fitting حسب Arno Förster (8356044)

"…We derive a low-scaling <i>G</i>₀<i>W</i>₀ algorithm for molecules using pair atomic density fitting (PADF) and an imaginary time representation of the Green’s function and describe its implementation in the Slater type orbital (STO)-based Amsterdam density functional (ADF) electronic structure code. …"

Data_Sheet_1_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX حسب Raymond Salvador (813880)

"…<p>Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. …"

Data_Sheet_2_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX حسب Raymond Salvador (813880)

"…<p>Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. …"

Data_Sheet_3_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX حسب Raymond Salvador (813880)

"…<p>Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. …"