Deceptive Pollination Process - Modeling Based on Individual Agents

This study explores the interactions between deceptive plants and pollinators using Agent-Based Modeling (ABM) to overcome the limitations of traditional mathematical models. By treating plants and pollinators as independent agents with defined behaviors and incorporating deep reinforcement learning, agents adapt to environmental changes and optimize strategies. Simulated experiments reveal the dynamic evolution of these interactions, highlighting competition and cooperation patterns. The findings show that ABM effectively simulates the interactions between deceptive plants and pollinators, providing valuable insights and analytical tools for understanding complex ecological interactions and evolution.