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Entropy-Guided Exploration in AlphaZero: Enhancing MCTS with Information Gain for Strategic Decision Making

Published: 02 December 2024 Publication History

Abstract

AlphaZero has revolutionized AI game-playing, yet it may overlook actions crucial for long-term strategy. This paper introduces Entropy-Guided MCTS (EG-MCTS), enhancing AlphaZero by incorporating information theory into action selection. We propose an information gain metric based on game state entropy, integrating it into MCTS to balance immediate rewards with informational value. Our contributions include developing this metric, modifying neural network training, and systematically balancing information gain with existing criteria. Comprehensive evaluations across various games demonstrate EG-MCTS's improved performance in complex strategic scenarios, showing enhanced efficiency in game tree navigation and adaptation to unfamiliar situations. These findings suggest broader applications in sequential decision-making under uncertainty, advancing the development of more adaptable AI systems.

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      MLMI '24: Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI)
      August 2024
      306 pages
      ISBN:9798400717833
      DOI:10.1145/3696271
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 02 December 2024

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      Author Tags

      1. Game AI
      2. Information Theory
      3. Monte Carlo Tree Search
      4. Reinforcement Learning

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