Technical Note: \cal Q-Learning
Machine Learning
Self-Organizing Maps
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
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The self-organizing map (SOM) [1] reduces a large input space to a fixed-size output space. It is trained by means of an unsupervised learning algorithm. One application of the SOM is to transform a set of robot sensor readings into a state space suitable as the input for the reinforcement learning algorithm Q-learning [5]. We have implemented two different formulations of this concept [2][3] using the Lego Mindstorms NXT robot [8], a robot commonly used in undergraduate computer science courses. We compared the performance of our implementations against a traditional Q-learning implementation. We found that the number and type of sensors encoded by the SOM has a significant impact on the quality of the behavior the robot learns.