A Study of AI Population Dynamics with Million-agent Reinforcement Learning

Understand

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.

• Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population.
• To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature.
• We endow the agents with the intelligence based on deep reinforcement learning (DRL).