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Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents

Authors
Park, JeonghoLee, JuwonKim, TaehwanAhn, InkyungPark, Jooyoung
Issue Date
4월-2021
Publisher
MDPI
Keywords
predator and prey; population; co-evolution; ecosystem; reinforcement learning
Citation
ENTROPY, v.23, no.4
Indexed
SCIE
SCOPUS
Journal Title
ENTROPY
Volume
23
Number
4
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/128320
DOI
10.3390/e23040461
ISSN
1099-4300
Abstract
The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still remains a challenging task in computational biology. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Here we frame the co-evolution of predators and preys in an ecosystem as allowing agents to learn and evolve toward better ones in a manner appropriate for multi-agent reinforcement learning. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys.
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College of Science and Technology > Data Computational Sciences in Division of Applied Mathematical Sciences > 1. Journal Articles
College of Science and Technology > Department of Electro-Mechanical Systems Engineering > 1. Journal Articles

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Ahn, In Kyung
과학기술대학 (응용수리과학부 데이터계산과학전공)
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