Benefits of combining dimensional attention and working memory for partially observable reinforcement learning problems

Neuroscience provides a rich source of inspiration for new types of algorithms and architectures to employ when building AI and the resulting biologically-plausible approaches that provide formal, testable models of brain function. The working memory toolkit (WMtk), was developed to assist the integration of an artificial neural network (ANN)-based computational neuroscience model of working memory into reinforcement learning (RL) agents, mitigating the details of ANN design and providing a simple symbolic encoding interface. While the WMtk allows RL agents to perform well in partially-observable domains, it requires prefiltering of sensory information by the programmer: a task often delegated to dimensional attention mechanisms in other cognitive architectures. To fill this gap, we develop and test a biologically-plausible dimensional attention filter for the WMtk and validate model performance using a partially-observable 1D maze task. We show that the attention filter improves learning behavior in two ways by: 1) speeding up learning in the short-term, early in training and 2) developing emergent alternative strategies which optimize performance over the long-term.