Biological reinforcement learning simulation for natural enemy -host behavior: Exploring deep learning algorithms for population dynamics

Abstract

This study introduces a simulation of biological reinforcement learning to explore the behavior of natural enemies in the presence of host pests, aiming to analyze the population dynamics between natural enemies and insect pests within an ecological context. The simulation leverages on Q-learning, a reinforcement learning algorithm, to model the decision-making processes of both parasitoids/predators and pests, thereby assessing the impact of varying parasitism and predation rates on pest population growth. Simulation parameters, such as episode count, duration in months, steps, learning rate, and discount factor, were set arbitrarily. Environmental and reward matrices, representing climatic conditions, crop availability, and the rewards for different actions, were established for each month. Initial Q-tables for parasitoids/predators and pests, along with population arrays, were used to track population dynamics. • The simulation, illustrated through the Aphid-Ladybird beetle interaction case study over multiple episodes, includes a sensitivity analysis to evaluate the effects of different predation rates. • Findings reveal detailed population dynamics, phase relationships between predator and pest populations, and the significant influence of predation rates. • These insights contribute to a deeper understanding of ecological systems and inform potential pest management strategies.

Keywords

• Artificial intelligence for biological control