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This paper presents a Q-learning method that works in continuous domains. Other characteristics of our approach are the use of an incremental topology preserving map (ITPM) to partition the input space, and the incorporation of bias to initialize the learning process. A unit of the ITPM represents a limited region of the input space and maps it onto the(More)
This paper presents a very simple robotic experiment to illustrate how probabilistic reasoning may be used for sensory-motor systems. We will show how our robot may learn internal representations of its interactions with the environment, how it may predict the sensory result of a given action, how it may generate motor command to reach a wished sensory(More)