Proceedings of the seventh international conference (1990) on Machine learning
Technical Note: \cal Q-Learning
Machine Learning
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiple model-based reinforcement learning
Neural Computation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning to act using real-time dynamic programming
Artificial Intelligence
An online adaptation control system using mnSOM
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Intrinsic Motivation Systems for Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
Creating Brain-Like Intelligence
Creating Brain-Like Intelligence
Hi-index | 0.00 |
We propose two basal ganglia (BG) models for autonomous behavior learning: the BG system model and the BG spiking neural network model. These models were developed on the basis of reinforcement learning (RL) theories and neuroscience principals of behavioral learning. The BG system model focuses on problems with RL input selection and reward setting. This model assumes that parallel BG modules receive a variety of inputs. We also propose an automatic setting method of internal reward for this model. The BG spiking neural network model focuses on problems with biological neural network architecture, ambiguous inputs and the mechanism of timing. This model accounts for the neurophysiological characteristics of neurons and differential functions of the direct and indirect pathways. We demonstrate that the BG system model achieves goals in fewer trials by learning the internal state representation, whereas the BG spiking neural network model has the capacity for probabilistic selection of action. Our results suggest that these two models are a step toward developing an autonomous learning system.