{"type":"ScholarlyArticle","title":"Explainable Reinforcement Learning via a Causal World Model","authors":[{"name":"Zhongwei Yu","slug":"zhongwei-yu","given":"Zhongwei","family":"Yu","affiliation":"Institute of Automation , Chinese Academy of Sciences"},{"name":"Jingqing Ruan","slug":"jingqing-ruan","given":"Jingqing","family":"Ruan","affiliation":"Institute of Automation , Chinese Academy of Sciences"},{"name":"Dengpeng Xing","slug":"dengpeng-xing","given":"Dengpeng","family":"Xing","affiliation":"Institute of Automation , Chinese Academy of Sciences"}],"abstract":"Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In this paper, we develop a novel framework for explainable RL by learning a causal world model without prior knowledge of the causal structure of the environment. The model captures the influence of actions, allowing us to interpret the long-term effects of actions through causal chains, which present how actions influence environmental variables and finally lead to rewards. Different from most explanatory models which suffer from low accuracy, our model remains accurate while improving explainability, making it applicable in model-based learning. As a result, we demonstrate that our causal model can serve as the bridge between explainability and learning.","doi":"10.24963/ijcai.2023/505","url":"https://ijcai.org/proceedings/2023/505","pdf":"https://ijcai.org/proceedings/2023/0505.pdf","markdown":"https://ijcai.org/proceedings/2023/505.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/505","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":505,"pages":{"first":4540,"last":4548},"keywords":["Machine Learning: ML: Reinforcement learning","Machine Learning: ML: Explainable/Interpretable machine learning","Machine Learning: ML: Causality"],"videos":[],"references":[{"position":1,"raw":"[Amir and Amir, 2018] Dan Amir and Ofra Amir. 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