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Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies.
Improving robot controller transparency through autonomous policy explanation
Hayes, B. and Shah, J. A · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Extrapolation and learning equations
Martius, G. and Lampert, C. H · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Rationalization: A neural machine translation approach to generating natural language explanations
Ehsan, U., Harrison, B., Chan, L., and Riedl, M. O · 2018
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Visualizing and understanding atari agents
Greydanus, S., Koul, A., Dodge, J., and Fern, A · 2018
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Deepproblog: Neural probabilistic logic programming
Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., and De Raedt, L · 2018
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Learning equations for extrapolation and control
Sahoo, S., Lampert, C., and Martius, G · 2018
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Programmatically interpretable reinforcement learning
Verma, A., Murali, V., Singh, R., Kohli, P., and Chaudhuri, S · 2018
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Evolving simple programs for playing atari games
Wilson, D. G., Cussat-Blanc, S., Luga, H., and Miller, J. F · 2018
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Distilling deep reinforcement learning policies in soft decision trees
Coppens, Y., Efthymiadis, K., Lenaerts, T., Nowé, A., Miller, T., Weber, R., and Magazzeni, D · 2019
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Sdrl: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning
Lyu, D., Yang, F., Liu, B., and Gustafson, S · 2019
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Imitation-projected programmatic reinforcement learning
Verma, A., Le, H., Yue, Y., and Chaudhuri, S · 2019
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Verbal explanations for deep reinforcement learning neural networks with attention on extracted features
Wang, X., Yuan, S., Zhang, H., Lewis, M., and Sycara, K · 2019
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Closed loop neural-symbolic learning via integrating neural perception, grammar parsing, and symbolic reasoning
Li, Q., Huang, S., Hong, Y., Chen, Y., Wu, Y. N., and Zhu, S.-C · 2020
Cited alongside, same era.
Space: Unsupervised object-oriented scene representation via spatial attention and decomposition
Lin, Z., Wu, Y.-F., Peri, S. V., Sun, W., Singh, G., Deng, F., Jiang, J., and Ahn, S · 2020
Cited alongside, same era.
Distribution-balanced loss for multi-label classification in long-tailed datasets
Wu, T., Huang, Q., Liu, Z., Wang, Y., and Lin, D · 2020
Cited alongside, same era.
Deep reinforcement learning at the edge of the statistical precipice
Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A. C., and Bellemare, M · 2021
Cited alongside, same era.
Discovering symbolic policies with deep reinforcement learning
Landajuela, M., Petersen, B. K., Kim, S., Santiago, C. P., Glatt, R., Mundhenk, N., Pettit, J. F., and Faissol, D · 2021
Cited alongside, same era.
Decoupling features in hierarchical propagation for video object segmentation
Yang, Z. and Yang, Y · 2022
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Pre-trained image encoder for generalizable visual reinforcement learning
Yuan, Z., Xue, Z., Yuan, B., Wang, X., Wu, Y., Gao, Y., and Xu, H · 2022
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Zheng, W., Sharan, S., Fan, Z., Wang, K., Xi, Y., and Wang, Z · 2022
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Language models can explain neurons in language models
Bills, S., Cammarata, N., Mossing, D., Tillman, H., Gao, L., Goh, G., Sutskever, I., Leike, J., Wu, J., and Saunders, W · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al · 2023
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Look wide and interpret twice: Improving performance on interactive instruction-following tasks
Nguyen, V.-Q., Suganuma, M., and Okatani, T · 2021
Cited alongside, same era.
Iterative bounding mdps: Learning interpretable policies via non-interpretable methods
Topin, N., Milani, S., Fang, F., and Veloso, M · 2021
Cited alongside, same era.
Off-policy differentiable logic reinforcement learning
Zhang, L., Li, X., Wang, M., and Tian, A · 2021
Cited alongside, same era.
Magnetic control of tokamak plasmas through deep reinforcement learning
Degrave, J., Felici, F., Buchli, J., Neunert, M., Tracey, B., Carpanese, F., Ewalds, T., Hafner, R., Abdolmaleki, A., de Las Casas, D., et al · 2022
Cited alongside, same era.
Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms
Huang, S., Dossa, R. F. J., Ye, C., Braga, J., Chakraborty, D., Mehta, K., and Araújo, J. G · 2022
Cited alongside, same era.
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Li, Q., Peng, Z., Feng, L., Zhang, Q., Xue, Z., and Zhou, B · 2022
Cited alongside, same era.
A survey of explainable reinforcement learning
Milani, S., Topin, N., Veloso, M., and Fang, F · 2022
Cited alongside, same era.
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Explainable reinforcement learning for broad-xai: a conceptual framework and survey
Dazeley, R., Vamplew, P., and Cruz, F · 2023
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Improving object-centric learning with query optimization
Jia, B., Liu, Y., and Huang, S · 2023
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Are large language models post hoc explainers?
Kroeger, N., Ley, D., Krishna, S., Agarwal, C., and Lakkaraju, H · 2023
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Explaining black box text modules in natural language with language models
Singh, C., Hsu, A. R., Antonello, R., Jain, S., Huth, A. G., Yu, B., and Gao, J · 2023
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Demystifying embedding spaces using large language models
Tennenholtz, G., Chow, Y., Hsu, C.-W., Jeong, J., Shani, L., Tulepbergenov, A., Ramachandran, D., Mladenov, M., and Boutilier, C · 2023
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An investigation into pre-training object-centric representations for reinforcement learning
Yoon, J., Wu, Y.-F., Bae, H., and Ahn, S · 2023
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Explaining agent behavior with large language models
Zhang, X., Guo, Y., Stepputtis, S., Sycara, K., and Campbell, J · 2023
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Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., and Wang, J · 2023
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Neural-symbolic recursive machine for systematic generalization
Li, Q., Zhu, Y., Liang, Y., Wu, Y. N., Zhu, S.-C., and Huang, S · 2024
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