The effect of synaptic weight initialization in feature-based successor representation learning
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After discovering place cells, the idea of the hippocampal (HPC) function to represent geometric spaces has been extended to predictions, imaginations, and conceptual cognitive maps. Recent research arguing that the HPC represents a predictive map; and it has shown that the HPC predicts visits to specific locations. This predictive map theory is based on successor representation (SR) from reinforcement learning. Feature-based SR (SF), which uses a neural network as a function approximation to learn SR, seems more plausible neurobiological model. However, it is not well known how different methods of weight (W) initialization affect SF learning. In this study, SF learners were exposed to simple maze environments to analyze SF learning efficiency and W patterns pattern changes. Three kinds of W initialization pattern were used: identity matrix, zero matrix, and small random matrix. The SF learner initiated with random weight matrix showed better performance than other three RL agents. We will discuss the neurobiological meaning of SF weight matrix. Through this approach, this paper tried to increase our understanding of intelligence from neuroscientific and artificial intelligence perspective.
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