Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents

Abstract

The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. 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. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. 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. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys.

An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

“…As a result, the pursuitevasion issue was converted into a zero-sum game addressed through minimax-Q learning. In predatory games, Park et al [17] set up a co-evolution framework for predator and prey to allow multiple agents to learn good policies by deep reinforcement learning. Gu et al [18] presented an attention-based fault-tolerant model, which could also be applied to pursuit-evasion games, and the key idea was to utilize the multihead attention mechanism to select the correct and useful information for estimating the critics.…”