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- W2373032581 abstract "The topology structure of neural network is very important for the effectiveness of neural network.The main difficulty in network modeling is how to control the topology evolution trend and complexity in network model efficiently.An evolutionary artificial neural network(RL-EANN) based on reinforcement learning is proposed in this paper.The performance of interaction between the network evolution group and its environment is evaluated by reinforcement learning.The reinforcement learning can be evolved without any priori knowledge,and the restriction of reinforcement signal is used to control the topology evolution trend of network group.It has been proved in the experiment of mobile robot obstacle avoidance navigation.The robot can efficiently learn obstacle avoidance and reaching target rapidly in uncertain environments with RL-EANN.The experiment achieves a good navigation performance.Experimental result proves that this method is rational and effective." @default.
- W2373032581 created "2016-06-24" @default.
- W2373032581 creator A5032443780 @default.
- W2373032581 date "2010-01-01" @default.
- W2373032581 modified "2023-09-23" @default.
- W2373032581 title "Evolutionary artificial neural networks based on reinforcement learning and its application in robot navigation" @default.
- W2373032581 hasPublicationYear "2010" @default.
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