Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

Aqeel Labash; Florian Fletzer; Daniel Majoral; Raul Vicente

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arXiv · 2307.12143

Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

Aqeel Labash·Florian Fletzer·Daniel Majoral·Raul Vicente

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Abstract

Adapting to regularities of the environment is critical for biological organisms to anticipate events and plan. A prominent example is the circadian rhythm corresponding to the internalization by organisms of the $24$-hour period of the Earth's rotation. In this work, we study the emergence of circadian-like rhythms in deep reinforcement learning agents. In particular, we deployed agents in an environment with a reliable periodic variation while solving a foraging task. We systematically characterize the agent's behavior during learning and demonstrate the emergence of a rhythm that is endogenous and entrainable. Interestingly, the internal rhythm adapts to shifts in the phase of the environmental signal without any re-training. Furthermore, we show via bifurcation and phase response curve analyses how artificial neurons develop dynamics to support the internalization of the environmental rhythm. From a dynamical systems view, we demonstrate that the adaptation proceeds by the emergence of a stable periodic orbit in the neuron dynamics with a phase response that allows an optimal phase synchronisation between the agent's dynamics and the environmental rhythm.

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Aqeel Labash, Florian Fletzer, Daniel Majoral, Raul Vicente. 2023-07-22. Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning. https://arxiv.org/abs/2307.12143

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