Self-organizing maps
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiple model-based reinforcement learning
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
An online adaptation control system using mnSOM
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
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Future robots/agents will need to perform situation specific behaviors for each user. To cope with diverse and unexpected situations, model-free behavioral learning is required. We have constructed a modular neural network model based on reinforcement learning and demonstrated that the model can learn multiple kinds of state transitions with the same architectures and parameter values, and without pre-designed models of environments. We recently developed a modular neural network model equipped with a modified on-line modular learning algorithm, which is more suitable for neural networks and more efficient in learning. This paper describes the performances of constructed models using the probabilistically fluctuated Markov decision process including partially observable conditions. In the test transitions, the observed states probabilistically fluctuated. The new learning model is able to function in those complex transitions without specific adjustments for each transition.