A differential Hebbian framework for biologically-plausible motor control
| Autor: | Sergio Verduzco-Flores, William Dorrell, Erik De Schutter |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Science::Machine Learning
Cognitive Neuroscience Models Neurological FOS: Physical sciences Feedback control Synaptic plasticity Nonlinear Sciences - Adaptation and Self-Organizing Systems Feedback Motor control Artificial Intelligence Quantitative Biology - Neurons and Cognition FOS: Biological sciences Reinforcement learning Learning Neurons and Cognition (q-bio.NC) Neural Networks Computer Reinforcement Psychology Adaptation and Self-Organizing Systems (nlin.AO) |
| DOI: | 10.48550/arxiv.2006.13471 |
| Popis: | In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control can reduce the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing learning of more complex actions. We use simple examples to illustrate our approach, and discuss how it could be extended to hierarchical architectures. Comment: 35 pages, 10 figures. Appendix: 9 pages, 2 figures |
| Databáze: | OpenAIRE |
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