Massimo Cencini: Learning pursuing and evasion strategies at low Reynolds number
We investigate an idealized prey-predator problem in a low Reynolds hydro-dynamic environment using reinforcement learning techniques. The problem is formalized in a game theoretic framework. Two microswimmers (the agents) — the pursuer (predator) and the evader (prey) — play the following game: the pursuer has to capture the evader in the shortest possible time and the latter to stay away from its predator as long as possible. The game terminates either upon capture (pursuer wins) or if the game duration exceeds a given time (evader wins). To accomplish its goal each agent is equipped with limited steering abilities and is capable to sense the hydrodynamic disturbances generated by the swimming opponent, which provide only partial information on its position and direction of motion. Such hydrodynamic disturbances also modify the motion of the microswimmers, making the environment dynamically complex. We show that learning through reinforcement both agents find nontrivial and co-evolving (with the learning process) strategies to accomplish their goals.
REFERENCE: F. Borra, L. Biferale, M. Cencini, and A. Celani. "Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number." arXiv preprint arXiv:2106.08609 (2021).
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