Explaining sequences of actions in multi-agent deep reinforcement learning models

• Main Authors: KHAING, Phyo Wai, GENG, Minghong, PATERIA, Shubham, SUBAGDJA, Budhitama, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Multi-agent Deep Reinforcement Learning; Explainable Artificial Intelligence; Sequential Decision Making; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9783; https://ink.library.smu.edu.sg/context/sis_research/article/10783/viewcontent/p2537__1_.pdf
• Institution: Singapore Management University

This paper introduces a method to explain MADRL agents’ behaviors by abstracting their actions into high-level strategies. Particularly, a spatio-temporal neural network model is applied to encode the agents’ sequences of actions as memory episodes wherein an aggregating memory retrieval can general...