Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study From an Optimization Perspective

Xin Li, Haojie Lei, Li Zhang, Mingzhong Wang

Open source

DOI
10.1109/tpami.2023.3285634
Published
2023-10
Container
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Open access
unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1109/tpami.2023.3285634,
  title = {Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study From an Optimization Perspective},
  author = {Xin Li and Haojie Lei and Li Zhang and Mingzhong Wang},
  year = {2023},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  doi = {10.1109/tpami.2023.3285634},
  url = {https://doi.org/10.1109/tpami.2023.3285634}
}

RIS

TY  - JOUR
TI  - Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study From an Optimization Perspective
AU  - Xin Li
AU  - Haojie Lei
AU  - Li Zhang
AU  - Mingzhong Wang
PY  - 2023
JO  - IEEE Transactions on Pattern Analysis and Machine Intelligence
DO  - 10.1109/tpami.2023.3285634
UR  - https://doi.org/10.1109/tpami.2023.3285634
ER  -

APA

Li, X., Lei, H., Zhang, L., & Wang, M. (2023). Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study From an Optimization Perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2023.3285634

Source records