Autotaxis of a run-and-tumble agent via reinforcement learning
Phys. Rev. E 114, 054402 – Published 3 November, 2026
DOI: https://doi.org/10.1103/4xnp-dsn4
Abstract
We study the run-and-tumble motion of a single autochemotactic agent that secretes and senses chemical cues using a reinforcement learning (RL) framework. In a chemical environment that changes dynamically via secretion, diffusion, and degradation of attractant chemicals, we study the effectiveness of RL for a successful navigation strategy. We show that RL enhances chemotactic performance. Starting from a random initial position, the probability of finding the agent in the favorable region (near the attractant peak) increases faster with time and reaches a higher value in the long-time limit when RL is used, compared to the case when the agent performs usual run-and-tumble chemotaxis without using RL. We also characterize the chemotactic performance of the agent by varying different parameters like rates of secretion, degradation, and diffusion. Importantly, we observe a nonmonotonic dependence of the chemotactic performance on the learning and exploration rates, indicating the existence of an optimal RL strategy for the most efficient navigation. We explain these observations as a result of the interplay between the preexisting attractant environment and the agent-generated attractant profile.
