Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Practical Issues in Temporal Difference Learning
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
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A motor control model based on reinforcement learning (RL) is proposed here. The model is inspired by organizational principles of the cerebral cortex, specifically on cortical maps and functional hierarchy in sensory and motor areas of the brain. Self-Organizing Maps (SOM) have proven to be useful in modeling cortical topological maps. The SOM maps the input space in response to the real-valued state information, and a second SOM is used to represent the action space. We use the Q-learning algorithm with a neighborhood update function, and an SOM for Q-function to avoid representing very large number of states or continuous action space in a large tabular form. The final model can map a continuous input space to a continuous action space.