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Poster
Dynamic allocation of limited memory resources in reinforcement learning
Nisheet Patel · Luigi Acerbi · Alexandre Pouget

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #965

Biological brains are inherently limited in their capacity to process and store information, but are nevertheless capable of solving complex tasks with apparent ease. Intelligent behavior is related to these limitations, since resource constraints drive the need to generalize and assign importance differentially to features in the environment or memories of past experiences. Recently, there have been parallel efforts in reinforcement learning and neuroscience to understand strategies adopted by artificial and biological agents to circumvent limitations in information storage. However, the two threads have been largely separate. In this article, we propose a dynamical framework to maximize expected reward under constraints of limited resources, which we implement with a cost function that penalizes precise representations of action-values in memory, each of which may vary in its precision. We derive from first principles an algorithm, Dynamic Resource Allocator (DRA), which we apply to two standard tasks in reinforcement learning and a model-based planning task, and find that it allocates more resources to items in memory that have a higher impact on cumulative rewards. Moreover, DRA learns faster when starting with a higher resource budget than what it eventually allocates for performing well on tasks, which may explain why frontal cortical areas in biological brains appear more engaged in early stages of learning before settling to lower asymptotic levels of activity. Our work provides a normative solution to the problem of learning how to allocate costly resources to a collection of uncertain memories in a manner that is capable of adapting to changes in the environment.

Author Information

Nisheet Patel (University of Geneva)
Luigi Acerbi (University of Helsinki)

Assistant professor Luigi Acerbi leads the *Machine and Human Intelligence* group at the Department of Computer Science of the University of Helsinki. His research spans Bayesian machine learning and computational and cognitive neuroscience. He is member of the *Finnish Centre for Artificial Intelligence* (FCAI), of the *International Brain Laboratory*, of ELLIS (*European Laboratory for Learning and Intelligent Systems*), and an off-site visiting scholar at New York University.

Alexandre Pouget (University of Geneva)

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