Simple Reinforcement Learning Agents: Pareto Beats Nash in an Algorithmic Game Theory Study
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Operations, Information and Decisions Papers
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Subject
Q-learning
algorithmic game theory
games
learning and games
Other Social and Behavioral Sciences
Set Theory
Theory and Algorithms
algorithmic game theory
games
learning and games
Other Social and Behavioral Sciences
Set Theory
Theory and Algorithms
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Kimbrough, Steven. O
Lu, Ming
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Abstract
Repeated play in games by simple adaptive agents is investigated. The agents use Q-learning, a special form of reinforcement learning, to direct learning of behavioral strategies in a number of 2×2 games. The agents are able effectively to maximize the total wealth extracted. This often leads to Pareto optimal outcomes. When the rewards signals are sufficiently clear, Pareto optimal outcomes will largely be achieved. The effect can select Pareto outcomes that are not Nash equilibria and it can select Pareto optimal outcomes among Nash equilibria.
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2005-03-01
Journal title
Information Systems and e-Business Management