Description
The development of autonomous agents capable of sophisticated strategic decision-making in complex environments is a central goal of artificial intelligence. This paper proposes a framework for discovering and interpreting strategies in a simulated grid-world battlefield environment. We leverage the AlphaZero algorithm, a powerful reinforcement learning method that combines Monte Carlo Tree Search (MCTS) with deep neural networks, to train agents. Crucially, the neural network component incorporates a Transformer architecture. The primary contribution of this work lies in the proposed methodology to utilize the self-attention mechanisms within the Transformer to gain insights into the agent’s decision-making process, specifically by visualizing which parts of the battlefield the network pays attention to when selecting an action. This approach aims not only to develop high-performing agents but also to enhance the interpretability of their learned strategies.| Période | 25 nov. 2025 |
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| Titre de l'événement | 1st International Conference on Big Data Analytics and Applications |
| Type d'événement | Une conférence |
| Numéro de conférence | 1 |
| Emplacement | Innsbruck, AutricheAfficher sur la carte |
| Degré de reconnaissance | International |
Contenu connexe
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Résultat de recherche
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Interpreting Tactical Decision-making in Transformer-based Agents
Résultats de recherche: Chapitre dans un livre, un rapport, des actes de conférences › Contribution à une conférence › Revue par des pairs