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arXiv Machine Learning By Yanming Zhang, Eric Papenhausen, Klaus Mueller

CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning

Read the original on arXiv Machine Learning →

CausalXRL is a new framework that makes reinforcement learning agents explainable by using causal graph reasoning. It offers transparent, structured, multi‑level explanations of agent decisions. The authors validate the approach with quantitative tests on benchmark RL environments and a qualitative expert study on the real‑time strategy game MicroRTS, showing faithful explanations and efficient causal‑structure learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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