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Evolution with reinforcement learning in negotiation.

Yi Zou1, Wenjie Zhan1, Yuan Shao1

  • 1School of Management, Huazhong University of Science and Technology, Wuhan, P.R. China.

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Summary
This summary is machine-generated.

Adaptive behavior and negotiation strategies improve long-term predictions. Combining evolutionary algorithms with reinforcement learning (RL) enhances collective performance and stable agent habits over time.

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • Evolutionary Game Theory

Background:

  • Adaptive behavior relies on long-term predictions, yet existing models focus on short-term dynamics.
  • Population dynamics research on behavioral adjustment has historically overlooked long-term strategy evolution.

Purpose of the Study:

  • To investigate long-term collective performance and strategy evolution in agent populations.
  • To model agent decision-making using a blend of historical and current information.
  • To compare the efficacy of reinforcement learning against classic evolutionary algorithms in negotiation dynamics.

Main Methods:

  • A hybrid model integrating evolutionary algorithms and reinforcement learning (RL).
  • Agents employ RL with a tradeoff between historical and current data for strategy adjustment.
  • Simulations of repeated interactions to observe strategy evolution and population convergence.

Main Results:

  • Agent strategies converge to stable states within populations.
  • Agents develop consistent negotiation habits over repeated interactions.
  • Reinforcement learning agents demonstrate superior performance in payoff, fairness, and stability.

Conclusions:

  • Long-term adaptive behavior is more robust than short-term adjustments.
  • The synergy of evolutionary algorithms and RL effectively models complex negotiation dynamics.
  • Reinforcement learning offers a significant advantage for improving agent performance and stability in adaptive systems.