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A new study by a group of researchers at ETH Zurich has modeled the swarm behavior of fish in order to apply the conclusions in the operation of autonomous robotic vehicles swarm. Using deep reinforcement learning, the group studied how fish draw energy from water flow and turbulence created by their own swimming schoolmates, gaining insights that could lead to low-energy, collective autonomous drone swarms.

Reinforcement learning is a field of machine learning inspired by behaviorist psychology. It’s broadly concerned with the existence and characterization of optimal solutions to a problem; it’s a way of teaching software agents to find the best solutions in an environment to achieve a reward.

Understanding the environment fish navigate is the key to understanding schooling behavior. “There is evidence that their swimming behavior adapts to flow gradients (rheotaxis) and in certain cases, it reflects energy harvesting from such environments,” the authors write. “Hydrodynamic interactions have also been implicated in the fish schooling patterns that form when individual fish adapt their motion to that of their peers, while compensating for flow-induced displacements.”

To prove whether this is the case, the model combined reinforcement learning with direct numerical simulations of the Navies stokes equations for two self-propelled autonomous swimmers in tandem, one leading and one following.

By comparing energetics data for the interacting fish and the solitary swimmers, the researchers also determined that the swimming efficiency of the interacting fish was significantly higher, according to