  Improving the numerical method of fish autonomous swimming behavior in
complex environments is of great significance to the optimization of bionic
controller,the design of fish passing facilities and the study of fish
behavior.This work has built a fish autonomous swimming simulation
platform,which adapts the high-precision IB-LBM to simulate the dynamic process
of the interaction between the fish and the flow field in real time,and
realizes the fish brain motion control through the SAC deep reinforcement
learning algorithm.More importantly,in view of the poor generalization of the
existing simulation platform,a method to simulate the fish's lateral line
function is proposed.By adding the Lateral-line machine and designing the
Macro-action system,the intelligent fish has the ability to
recognize,classify,memorize and transplant the corresponding swimming strategy
in the unsteady field.Using this method,the training and simulation of
point-to-point predation swimming and Kamangait test under different inlet
velocities are carried out.In the example of point-to-point predation
swimming,the fish in random position can adjust the swimming posture and speed
autonomously to catch the fast moving food,and has a certain prediction ability
on the movement trajectory of the food.In the Kaman-gait test,the trained fish
are placed in three different Kamangait flow fields,to study its ability to
recognize the flow field and select swimming strategies through experience.The
results of numerical experiments show that,comparing with the other value
function networks,the SAC algorithm based on maximum entropy has more
advantages in convergence speed and training efficiency when simulating fish
brain decision-making.The use of the Lateral line Machine and Macro-action
system can avoid the waste of experience and improve the adaptability of
intelligent fish in the new complex flow field environment.
