Interpreting Tactical Decision-Making in Transformer-Based Agents

Beschreibung

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.

Zeitraum	25 Nov. 2025
Ereignistitel	1st International Conference on Big Data Analytics and Applications
Veranstaltungstyp	Konferenz
Konferenznummer	1
Ort	Innsbruck, ÖsterreichAuf Karte anzeigen
Bekanntheitsgrad	International