Explainable Reinforcement Learning via a Causal World Model
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909076313604096 |
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| author | Yu, Zhongwei Ruan, Jingqing Xing, Dengpeng |
| author_facet | Yu, Zhongwei Ruan, Jingqing Xing, Dengpeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02749 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Explainable Reinforcement Learning via a Causal World Model Yu, Zhongwei Ruan, Jingqing Xing, Dengpeng Machine Learning 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. |
| title | Explainable Reinforcement Learning via a Causal World Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.02749 |