DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning

Yuko Ishiwaka, Xiao Steven Zeng, Shun Ogawa, Donovan Michael Westwater, Tadayuki Tone, Masaki Nakada

Keywords: Bio-inspired, Fish Schooling, Physically Based Simulation, Deep Reinforcement Learning, Adaptive

TL;DR: We introduce bio-inspired fish simulation. Deep reinforcement learning was applied for the fish to be able to learn efficient schooling behavior in various fish cages and adapt themselves to the change of the environment.

Abstract: Our goal is to synthesize realistic underwater scenes with various fish species in different fish cages, which can be utilized to train computer vision models to automate fish counting and sizing tasks. It is a challenging problem to prepare a sufficiently diverse labeled dataset of images from aquatic environments. We solve this challenge by introducing an adaptive bio-inspired fish simulation. The behavior of caged fish changes based on the species, size and number of fish, and the size and shape of the cage, among other variables. However, a method to autonomously achieve schooling behavior for caged fish did not exist. In this paper, we propose a method for achieving schooling behavior for any given combination of variables, using multi-agent deep reinforcement learning (DRL) in various fish cages in arbitrary environments. Furthermore, to visually reproduce the underwater scene in different locations and seasons, we incorporate a physically-based underwater simulation.