Abstract
Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in
complex environments. Here we investigate whether reinforcement learning can provide insights into
biological systems when trained to perform chemotaxis. Namely, whether we can learn about how
intelligent agents process given information in order to swim towards a target. We run simulations
covering a range of agent shapes, sizes, and swim speeds to determine if the physical constraints
on biological swimmers, namely Brownian motion, lead to regions where reinforcement learners’
training fails. We find that the RL agents can perform chemotaxis as soon as it is physically possible
and, in some cases, even before the active swimming overpowers the stochastic environment. We
study the efficiency of the emergent policy and identify convergence in agent size and swim speeds.
Finally, we study the strategy adopted by the reinforcement learning algorithm to explain how the
agents perform their tasks. To this end, we identify three emerging dominant strategies and several
rare approaches taken. These strategies, whilst producing almost identical trajectories in simulation,
are distinct and give insight into the possible mechanisms behind which biological agents explore
their environment and respond to changing conditions.
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