Learning in Brains and Machines Using Ornstein–Uhlenbeck Adaptation
Author Information
Author(s): García Fernández Jesús, Ahmad Nasir, van Gerven Marcel, Kotsiantis Sotiris
Primary Institution: Donders Institute for Brain, Cognition and Behaviour, Radboud University
Hypothesis
Can Ornstein–Uhlenbeck adaptation serve as an effective learning mechanism for intelligent systems?
Conclusion
Ornstein–Uhlenbeck adaptation provides a promising alternative to traditional gradient-based learning methods, showing effectiveness in various tasks.
Supporting Evidence
- OUA effectively learns in supervised and reinforcement learning settings.
- OUA can adapt hyper-parameters autonomously.
- OUA shows potential applications in neuromorphic computing.
- Learning dynamics are validated across various tasks including weather prediction.
Takeaway
This study shows a new way for machines to learn by using randomness, similar to how our brains might learn from mistakes.
Methodology
The study uses Ornstein–Uhlenbeck processes to adapt learning parameters based on global reinforcement signals and validates the approach through various tasks.
Limitations
The stochastic nature of the algorithm may lead to fluctuations in parameter values rather than exact convergence.
Digital Object Identifier (DOI)
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