Self-organizing neural architecture for reinforcement learning
Self-organizing neural networks are typically associated with unsupervised learning. This paper presents a self-organizing neural architecture, known as TD-FALCON, that learns cognitive codes across multi-modal pattern spaces, involving sensory input, actions, and rewards, and is capable of adapting...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2006
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/6833 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Self-organizing neural networks are typically associated with unsupervised learning. This paper presents a self-organizing neural architecture, known as TD-FALCON, that learns cognitive codes across multi-modal pattern spaces, involving sensory input, actions, and rewards, and is capable of adapting and functioning in a dynamic environment with external evaluative feedback signals. We present a case study of TD-FALCON on a mine avoidance and navigation cognitive task, and illustrate its performance by comparing with a state-of-the-art reinforcement learning approach based on gradient descent backpropagation algorithm |
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