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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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Incorporating social payoff into reinforcement learning promotes cooperation
Litong Fan1, Zhao Song1, Lu Wang1
1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.
Chaos (Woodbury, N.Y.)
|January 1, 2023
Summary
Self-regarding Q-learning, incorporating social payoff, promotes cooperation in spatial prisoner's dilemma games. This reinforcement learning approach enables agents to learn cooperative strategies, even with high temptation to defect.
Area of Science:
- Game Theory
- Computational Economics
- Artificial Intelligence
Background:
- Reinforcement learning (RL) models agent strategy dynamics.
- Traditional Q-learning struggles with cooperation in homogeneous populations.
- Spatial prisoner's dilemma games are a key model for studying cooperation.
Purpose of the Study:
- Investigate the effect of self-regarding Q-learning with social payoff on cooperation.
- Redefine the Q-learning reward function to include social payoff.
- Analyze the emergence and stability of cooperative strategies.
Main Methods:
- Modified self-regarding Q-learning incorporating social payoff.
- Numerical simulations on spatial prisoner's dilemma games.
- Analysis of sublattice-ordered structures and payoff distributions.
Main Results:
- Social payoff incorporation significantly facilitates cooperation.
- Self-regarding Q-learning allows coexistence of cooperators and defectors.
- A checkerboard pattern emerges, enhancing agent payoffs.
- The mechanism shows robustness across different network structures.
Conclusions:
- Redefined Q-learning with social payoff is effective for promoting cooperation.
- This approach supports stable coexistence of diverse strategies.
- The findings offer insights into emergent cooperative behaviors in complex systems.
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