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Incorporating social payoff into reinforcement learning promotes cooperation.

Chaos (Woodbury, N.Y.)·2023
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Spatial reciprocity under reinforcement learning mechanism.

Lu Wang1, Xiaoqiu Shi1,2, Yang Zhou3

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This study explores how local interactions affect cooperative behavior in agents using reinforcement learning. Spatial reciprocity in cooperation is fully realized only when learning agents and interacting agents overlap.

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Area of Science:

  • Artificial Intelligence
  • Game Theory
  • Social Science

Background:

  • Current research on agent cooperation under reinforcement learning often assumes global interactions or studies local interactions without formally addressing strategy limitations.
  • Agents interacting only locally face strategy choices influenced by network structures, a factor often overlooked.

Purpose of the Study:

  • To investigate cooperative behavior in agents within a social decision-making environment with conflicting individual and collective interests.
  • To analyze the impact of local versus global strategy learning on cooperation evolution.
  • To understand the role of spatial reciprocity in reinforcement learning dynamics.

Main Methods:

  • Utilized the prisoner's dilemma game model from game theory to represent social dilemmas.
  • Investigated the effects of local and global strategy learning on agent cooperation separately.
  • Examined agent interactions within network structures under reinforcement learning.

Main Results:

  • Network structure has limited impact on promoting cooperation when there's no inherent connection between interacting and learning agents.
  • Spatial reciprocity, a known effect in evolutionary game theory, is fully realized when there is an overlap between interacting and learning agents.
  • Local interaction limitations significantly influence agent strategy choices in cooperative dilemmas.

Conclusions:

  • The degree of overlap between interacting and learning agents is crucial for realizing spatial reciprocity in cooperative behavior.
  • Reinforcement learning mechanisms are sensitive to the structure of agent interactions when evolving cooperation.
  • Understanding local interaction dynamics is key to advancing research on artificial agent cooperation.