An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning

09/13/2017
by   Yaodong Yang, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 6

page 7

09/13/2017

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

We conduct an empirical study on discovering the ordered collective dyna...
12/02/2017

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

We introduce MAgent, a platform to support research and development of m...
02/09/2020

Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning

Simulation of population dynamics is a central research theme in computa...
04/26/2019

The Collective Intelligence for Advancing Communications

The fifth-generation cellular networks (5G) has boosted the unprecedente...
08/14/2021

A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning

Microscopic epidemic models are powerful tools for government policy mak...
04/01/2020

Development of swarm behavior in artificial learning agents that adapt to different foraging environments

Collective behavior, and swarm formation in particular, has been studied...
03/17/2020

Giving Up Control: Neurons as Reinforcement Learning Agents

Artificial Intelligence has historically relied on planning, heuristics,...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.