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Relational Reinforcement Learning (RRL) can offers various desirable features.
Q-learning
Watkins, C. J. and Dayan, P · 1992
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Top-down induction of first-order logical decision trees
Blockeel, H. and De Raedt, L · 1998
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Relational reinforcement learning
Džeroski, S., De Raedt, L., and Blockeel, H · 1998
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Combining active learning with inductive logic programming to close the loop in machine learning
Bryant, C., Muggleton, S., Page, C., Sternberg, M., et al · 1999
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Relational reinforcement learning
Džeroski, S., De Raedt, L., and Driessens, K · 2001
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A survey of reinforcement learning in relational domains
Van Otterlo, M · 2005
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Deep reinforcement learning with double q-learning
Van Hasselt, H., Guez, A., and Silver, D · 2016
Cited alongside, same era.
Learning to represent programs with graphs
Allamanis, M., Brockschmidt, M., and Khademi, M · 2017
Cited alongside, same era.
Rainbow: Combining improvements in deep reinforcement learning, 2017
Hessel, M., Modayil, J., van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D · 2017
Cited alongside, same era.
graph2vec: Learning distributed representations of graphs
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., and Jaiswal, S · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Learning explanatory rules from noisy data
Evans, R. and Grefenstette, E · 2018
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Stable baselines
Hill, A., Raffin, A., Ernestus, M., Gleave, A., Kanervisto, A., Traore, R., Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., and Wu, Y · 2018
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Decoding ldpc codes on binary erasure channels using deep recurrent neural-logic layers
Payani, A. and Fekri, F · 2018
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Relational deep reinforcement learning
Zambaldi, V., Raposo, D., Santoro, A., Bapst, V., Li, Y., Babuschkin, I., Tuyls, K., Reichert, D., Lillicrap, T., Lockhart, E., et al · 2018
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A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Neural logic reinforcement learning, 2019
Jiang, Z. and Luo, S · 2019
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Inductive logic programming via differentiable deep neural logic networks
Payani, A. and Fekri, F · 2019
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