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

Area of Science:

• Ecology and Evolutionary Biology
• Artificial Intelligence
• Computational Biology

Background:

• Co-evolutionary processes are crucial in ecology but challenging to model with traditional methods.
• Predicting the ecological consequences of evolution requires advanced computational approaches.
• Understanding adaptive learning in species interactions is key to ecosystem dynamics.

Purpose of the Study:

• To investigate the impact of learning mechanisms on predator-prey dynamics using AI.
• To simulate and analyze co-evolutionary processes in a large-scale ecosystem.
• To assess the role of deep reinforcement learning in shaping species behavior and ecosystem stability.

Main Methods:

• Utilized deep reinforcement learning algorithms to simulate organism learning.
• Employed Monte Carlo simulation for large-scale ecosystem evolution modeling.
• Integrated learning experiences into behavioral determination and inheritance.

Main Results:

• Predator reinforcement learning improved ecosystem stability and promoted coexistence strategies.
• Prey learning had a less positive impact and increased predator extinction risk.
• Inconsistent learning rates between prey and predators exacerbated extinction risks.
• Co-evolution led to population declines due to antagonistic evolutionary networks.
• Simultaneous invasion of learning predators and prey favored prey.

Conclusions:

• Learning mechanisms significantly influence predator-prey ecosystem dynamics.
• AI approaches, specifically deep reinforcement learning, are feasible for predicting evolutionary behavior.
• The study highlights the complex interplay between learning, co-evolution, and ecosystem stability.