A menu of designs for reinforcement learning over time
Neural networks for control
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A possibility for implementing curiosity and boredom in model-building neural controllers
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, the processes of exploration and of incremental learning in the robot navigation task are studied using the dynamical systems approach. A neural network model which performs the forward modeling, planning, consolidation learning and novelty rewarding is used for the robot experiments. Our experiments showed that the robot repeated a few variation of travel patterns in the beginning of the exploration, and later the robot explored more diversely in the workspace by combining and mutating the previously experienced patterns. Our analysis indicates that internal confusion due to immature learning plays the role of a catalyst in generating diverse action sequences. It is found that these diverse exploratory travels enable the robot to acquire the rational modeling of the environment in the end.