Reinforcement learning in complementarity game and population dynamics.

Area of Science:

• Game Theory
• Computational Economics
• Agent-Based Modeling

Background:

• Reinforcement learning (RL) models agent decision-making through trial and error.
• Complementarity games model strategic interactions between agents in distinct populations.
• Understanding adaptive strategies in these games is crucial for economic and social modeling.

Purpose of the Study:

• To systematically compare the performance of different reinforcement learning schemes.
• To evaluate these schemes within the context of a specific complementarity game.
• To contrast reinforcement learning with evolutionary strategies for insights into adaptation and exploration.

Main Methods:

• Implementation and testing of Roth-Erev, Bush-Mosteller, and SoftMax reinforcement learning algorithms.
• Utilizing a complementarity game framework as described by Jost and Li (2005).
• Comparison of reinforcement learning outcomes with those from evolutionary game theory approaches.

Main Results:

• A modified Roth-Erev scheme, featuring a power exponent of 1.5, demonstrated superior performance compared to standard versions and other tested RL schemes.
• The study identified trade-offs between rapid adaptation and systematic exploration inherent in different learning strategies.
• Reinforcement learning strategies were contrasted with evolutionary dynamics, highlighting differences in learning rates and adaptation.

Conclusions:

• The modified Roth-Erev reinforcement learning scheme offers an effective approach for modeling agent behavior in complementarity games.
• The research provides valuable insights into the dynamics of learning, adaptation, and exploration in strategic interactions.
• Findings contribute to the broader understanding of agent-based modeling and computational approaches in social sciences.