An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning
By Yaodong Yang, Lantao Yu, Yiwei Bai, Jun Wang, Weinan Zhang, Ying Wen, Yong Yu. 2017
7 citations recorded by the source corpus.
In this paper, we conduct an empirical study on discovering the ordered collective dynamics obtained by a population of artificial intelligence (AI) agents. Our intention is to put AI agents into a simulated natural context, and then to understand their induced dynamics at the population level. In particular, we aim to 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, and scale the population size up to millions. Our results show that the population dynamics of AI agents, driven only by each agent's individual self interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.
Subfields: Artificial Intelligence, Economics and Econometrics, Sociology and Political Science
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Citations in the corpus
- MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence (2017, background): “A key technology of ACI is multi-agent reinforcement learning (RL) but typically requires a scale of hundreds to millions of agents (Yang et al. 2017).”