![]() ![]() RL agents generally focus on building strategies for taking actions in an environment in order to maximize expected rewards. Recently, reinforcement learning (RL) has achieved impressive advances in areas, such as games. When the state transition rules and rewards are not stipulated in advance, as is typical in domains where reinforcement learning is applied, training is realized by means of interactions with the environment, which includes the responses of other agents, which enables learning agents to discover and evolve toward a better policy. Nevertheless, Monte Carlo simulation combined with the agent-based approach in two-dimensional space can often reveal more diverse and detailed spatiotemporal patterns arising in the domains under consideration. Contrary to the traditional tools of population dynamics modeling, the agent-based approach does not resort to directly modeling equilibrium through mathematics. The agent-based approach (e.g., References ) is based on mutually interacting agents via prescribed rules in a simulated environment, and can efficiently and conveniently describe individual and mutual behaviors together. Despite the modeling and/or predictive capacity inherent within these mathematical approaches, the study of population dynamics still remains a challenging task in computational biology because analyzing and accurately predicting the intelligent adaptation of interacting species is difficult, and it is often desirable to employ an agent-based perspective to shed more light on the topic. ![]() ![]() Many observations in nature demonstrate that the emergence of a predator or prey can induce the directed movement of a species thus, researchers have proposed and investigated several mathematical models along these lines. For a general explanation of discrete and continuous models on dispersal evolution, we refer the reader to References and references therein. Because various species usually migrate to a region to find a more favorable habitat that provides sufficient food and/or better conditions for survival, an understanding of dispersal strategy is critically important to the study of species evolution. For decades, researchers have developed dispersal theory based on the surrounding environment as an influential element, and the environment affecting a particular species includes elements, such as other interacting species. It is known that both the interaction between different species and the response of a species to its environment are necessary to develop a more realistic dispersal model for biological species. Many researchers have studied population models with evolutionary dispersal perspectives, such as dispersal depending on other species and starvation-driven diffusion depending on resources. The problem of addressing predator-prey interactions is an important field in ecology, and finding a reasonable population model for a predator-prey ecosystem is particularly important for understanding its dynamic features. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. 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. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. 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. The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. ![]()
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