Lifelong Reinforcement Learning via Neuromodulation

• URL: http://arxiv.org/abs/2408.08446v1
• Date: Thu, 15 Aug 2024 22:53:35 GMT
• Title: Lifelong Reinforcement Learning via Neuromodulation
• Authors: Sebastian Lee, Samuel Liebana Garcia, Claudia Clopath, Will Dabney,
• Abstract summary: Evolution has imbued animals and humans with highly effective adaptive learning functions and decision-making strategies. Central to these theories and integrating evidence from neuroscience into learning is the neuromodulatory system.
• Score: 13.765526492965853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Navigating multiple tasks$\unicode{x2014}$for instance in succession as in continual or lifelong learning, or in distributions as in meta or multi-task learning$\unicode{x2014}$requires some notion of adaptation. Evolution over timescales of millennia has imbued humans and other animals with highly effective adaptive learning and decision-making strategies. Central to these functions are so-called neuromodulatory systems. In this work we introduce an abstract framework for integrating theories and evidence from neuroscience and the cognitive sciences into the design of adaptive artificial reinforcement learning algorithms. We give a concrete instance of this framework built on literature surrounding the neuromodulators Acetylcholine (ACh) and Noradrenaline (NA), and empirically validate the effectiveness of the resulting adaptive algorithm in a non-stationary multi-armed bandit problem. We conclude with a theory-based experiment proposal providing an avenue to link our framework back to efforts in experimental neuroscience.

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