Next Article in Journal
Comparing Information Metrics for a Coupled Ornstein–Uhlenbeck Process
Previous Article in Journal
A New Technique Based on Voronoi Tessellation to Assess the Space-Dependence of Categorical Variables
Open AccessArticle

Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System

1,2, 1,2,* and 1,2
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin 999077, Hong Kong, China
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 773;
Received: 26 June 2019 / Revised: 27 July 2019 / Accepted: 6 August 2019 / Published: 8 August 2019
PDF [2297 KB, uploaded 8 August 2019]


Understanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators’ reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator–prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator–prey ecosystem using AI approaches. View Full-Text
Keywords: co-evolution; population dynamics; Monte Carlo simulation co-evolution; population dynamics; Monte Carlo simulation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, X.; Cheng, J.; Wang, L. Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System. Entropy 2019, 21, 773.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top