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B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
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Related Experiment Video

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Operant Learning of Drosophila at the Torque Meter
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All by Myself: Learning individualized competitive behavior with a contrastive reinforcement learning optimization.

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  • 1CONTACT Unit, Italian Institute of Technology, Italy.

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This study introduces a novel three-layer neural network model for competitive multi-agent reinforcement learning. The model learns personalized strategies to disrupt specific opponents, outperforming existing methods in game scenarios.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Game Theory

Background:

  • Multi-agent systems require agents to optimize goals while minimizing adversaries' objectives.
  • Existing solutions often lack personalized strategy development against individual opponents.
  • Competitive scenarios involve complex dynamics and require overcoming opponent strategies.

Purpose of the Study:

  • To propose a novel three-layer neural network model for competitive games.
  • To enable agents to learn game representations, map opponent strategies, and develop disruptive tactics.
  • To train the model online using contrastive optimization for competitive multiplayer games.

Main Methods:

  • A three-layer neural network architecture is proposed.
  • The model learns game representation, opponent strategy mapping, and disruption techniques.
  • Online training with a composed loss based on contrastive optimization is employed.

Main Results:

  • The model demonstrates superior performance against various baseline models (offline, online, competitive-specific).
  • Enhanced performance is observed when playing against the same opponent repeatedly.
  • The model effectively learns specific strategies for different game scenarios.

Conclusions:

  • The proposed model offers a significant advancement in personalized strategy learning for competitive multi-agent scenarios.
  • The approach is validated across diverse game environments, including Pokemon duels and the Chef's Hat card game.
  • This research contributes to developing more adaptive and effective AI agents in competitive settings.