  Simulation of population dynamics is a central research theme in
computational biology, which contributes to understanding the interactions
between predators and preys. Conventional mathematical tools of this theme,
however, are incapable of accounting for several important attributes of such
systems, such as the intelligent and adaptive behavior exhibited by individual
agents. This unrealistic setting is often insufficient to simulate properties
of population dynamics found in the real-world. In this work, we leverage
multi-agent deep reinforcement learning, and we propose a new model of
large-scale predator-prey ecosystems. Using different variants of our proposed
environment, we show that multi-agent simulations can exhibit key real-world
dynamical properties. To obtain this behavior, we firstly define a mating
mechanism such that existing agents reproduce new individuals bound by the
conditions of the environment. Furthermore, we incorporate a real-time
evolutionary algorithm and show that reinforcement learning enhances the
evolution of the agents' physical properties such as speed, attack and
resilience against attacks.
