Direct code access in self-organizing neural networks for reinforcement learning
TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this defici...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2007
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/6764 https://ink.library.smu.edu.sg/context/sis_research/article/7767/viewcontent/DA_FALCON_IJCAI07.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this deficiency, this paper presents a direct code access procedure whereby TD-FALCON conducts instantaneous searches for cognitive nodes that match with the current states and at the same time provide maximal reward values. Our comparative experiments show that TD-FALCON with direct code access produces comparable performance with the original TD-FALCON while improving significantly in computation efficiency and network complexity. |
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