RFR: Left/Right brain in an RL agent
Proposed by Cerenaut and the Whole Brain Architecture Initiative
(What is a Request for Research/RFR?)
The brains of all bilaterally symmetric animals on Earth are divided into left and right hemispheres. The anatomy and functionality of the hemispheres have a large degree of overlap, but they specialize to possess different attributes. This principle is poorly understood and has not been exploited in AI/ML. The right hemisphere is more dominant for novelty, and the left for routine [1, 2, 3]. Activity slowly moves to the left hemisphere as a task is perfected. In this project, we apply that principle to continual RL, where new tasks are introduced over time. We will create a ‘generalist’ right network that can perform novel tasks while a left network has time to become proficient, providing a more maintained level of competence across new tasks – a critical characteristic for practical agents to operate in realistic environments.
Machine Learning, Deep Learning or some knowledge and willingness to learn. Must have Python and some experience with PyTorch or Tensorflow.
 E. Goldberg, K. Podell, and M. Lovell, “Lateralization of frontal lobe functions and cognitive novelty,” Journal of Neuropsychiatry and Clinical Neurosciences, vol. 6, no. 4, pp. 371–378, 1994, doi: 10.1176/JNP.6.4.371.
 E. Goldberg and L. D. Costa, “Hemisphere differences in the acquisition and use of descriptive systems,” Brain and Language, vol. 14, no. 1, pp. 144–173, 1981, doi: 10.1016/0093-934X(81)90072-9.
 E. Goldberg et al., “Hemispheric asymmetries of cortical volume in the human brain,” Cortex, vol. 49, pp. 200–210, 2013, doi: 10.1016/j.cortex.2011.11.002.
Contact: rfr [at] wba-initiative.org