DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning
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.
Cite
Text
Ishiwaka et al. "DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning." Neural Information Processing Systems, 2022.Markdown
[Ishiwaka et al. "DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/ishiwaka2022neurips-deepfoids/)BibTeX
@inproceedings{ishiwaka2022neurips-deepfoids,
title = {{DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning}},
author = {Ishiwaka, Yuko and Zeng, Xiao and Ogawa, Shun and Westwater, Donovan and Tone, Tadayuki and Nakada, Masaki},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/ishiwaka2022neurips-deepfoids/}
}