Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

by   Aqeel Labash, et al.

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.


page 7

page 16

page 19


Adaptive patch foraging in deep reinforcement learning agents

Patch foraging is one of the most heavily studied behavioral optimizatio...

Evolutionary Reinforcement Learning Dynamics with Irreducible Environmental Uncertainty

In this work we derive and present evolutionary reinforcement learning d...

Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning

Advances in artificial intelligence often stem from the development of n...

Moderate Environmental Variation Promotes Adaptation in Artificial Evolution

In this paper we analyze the role of environmental variations in the evo...

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

In this work we identify the dormant neuron phenomenon in deep reinforce...

Incentivizing the Emergence of Grounded Discrete Communication Between General Agents

We converted the recently developed BabyAI grid world platform to a send...

Please sign up or login with your details

Forgot password? Click here to reset