Multi-objective Longitudinal Decision-making for Autonomous Electric Vehicle: A Entropy-constrained Reinforcement Learning Approach

2020 ◽  
Author(s):  
Xiangkun He ◽  
Cong Fei ◽  
Yulong Liu ◽  
Kaiming Yang ◽  
Xuewu Ji
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Electric Vehicle ◽  
Learning Approach ◽  
Multi Objective

scholarly journals DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Yash Khemchandani ◽  
Stephen O’Hagan ◽  
Soumitra Samanta ◽  
Neil Swainston ◽  
Timothy J. Roberts ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Multi Objective ◽  
Computational Strategy

scholarly journals Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1556 ◽  
Author(s):  
Cao ◽  
Zhang ◽  
Xiao ◽  
Hua
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Voltage Stability ◽  
Reactive Power ◽  
Learning Approach ◽  
Strong Impact ◽  
Reactive Power Optimization ◽  
Energy Internet ◽  
Transient Voltage Stability ◽  
Transient Voltage

The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

scholarly journals A reinforcement learning approach to autonomous decision-making in smart electricity markets

2013 ◽  
Vol 92 (1) ◽  
pp. 5-39 ◽  
Author(s):  
Markus Peters ◽  
Wolfgang Ketter ◽  
Maytal Saar-Tsechansky ◽  
John Collins
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Electricity Markets ◽  
Learning Approach ◽  
Autonomous Decision
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