Explain Reinforcement Learning Agents Through Fuzzy Rule Reconstruction
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023, pp. 1-6
- Issue Date:
- 2023-11-09
Embargoed
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Explain Reinforcement Learning Agents Through Fuzzy Rule Reconstruction.pdf | Accepted version | 1.67 MB |
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There has been a lot of interest in making reinforcement learning RL models more explainable as their applications become more widespread Currently most of the explainable RL xRL models focus on improving the transparency of the agent s observations rather than the relationship between the agent s states and actions This study introduces the Explainable Fuzzy Reconstruction Net EFRN which aims to interpret these relationships in RL The EFRN utilizes the interpretability of Fuzzy Neural Networks FNNs to create IF THEN rules and a generative model to showcase the learned knowledge The IF THEN rules can be expressed in a way that is easily understandable for humans such as IF A THEN B The generative model then visualizes the state as patterns that is easy for humans to comprehend The results of the study shows that the proposed EFRN maintains the same level of performance as traditional RL methods and significantly improves the explainability of the RL agents both globally and locally
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