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Stereoacuity Improvement using Random-Dot Video Games

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Reinforcement learning meets minority game: Toward optimal resource allocation.

Si-Ping Zhang^1,2, Jia-Qi Dong^2,3, Li Liu⁴

¹The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.

Physical Review. E

|April 20, 2019

Summary

Reinforcement learning (RL) in artificial intelligence (AI) eliminates herding in complex resource allocation. Agents learn optimal actions, driving systems toward efficient resource use with intermittent fluctuations.

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Area of Science:

Artificial Intelligence
Complex Systems
Game Theory

Background:

Herding behavior is a common issue in resource allocation systems.
External controls are often required to mitigate herding.
The application of artificial intelligence to complex systems is rapidly expanding.

Purpose of the Study:

To investigate the elimination of herding behavior in complex resource allocation systems using reinforcement learning (RL).
To demonstrate that RL-empowered agents can achieve optimal resource allocation without external control.
To analyze the dynamics and fluctuations during the system's evolution towards an optimal state.

Main Methods:

Exploiting reinforcement learning (RL), specifically Q-learning, within artificial intelligence (AI) agents.
Simulating complex resource allocation systems where agents learn optimal strategies.
Developing physical analysis and deriving mean-field equations to understand system dynamics.

Main Results:

Herding behavior is effectively eliminated when agents utilize RL to maximize payoffs.
Systems consistently evolve towards an optimal state of efficient resource utilization, irrespective of the initial conditions.
Intermittent large fluctuations occur during evolution, with their timing dependent on the parity of the evolution steps.

Conclusions:

Reinforcement learning provides a viable, self-governing solution for eliminating herding in resource allocation.
The developed RL-empowered minority game system exhibits predictable dynamics and broad applicability.
The findings contribute to understanding emergent behavior in complex adaptive systems through AI.