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We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and object-centric actions.
Aeronautiques, C., Howe, A., Knoblock, C., McDermott, I.D., Ram, A., Veloso, M., Weld, D., SRI, D.W., Barrett, A., Christianson, D., et al.: Pddl— the planning domain definition language. Technical Report, Tech. Rep. (1998)
1998
Earlier work this paper cites.
Džeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Machine learning 43
2001
Earlier work this paper cites.
Slaney, J., Thiébaux, S.: Blocks world revisited. Artificial Intelligence 125
2001
Earlier work this paper cites.
Guestrin, C., Koller, D., Parr, R., Venkataraman, S.: Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research 19
2003
Earlier work this paper cites.
Van Otterlo, M.: A survey of reinforcement learning in relational domains. Centre for Telematics and Information Technology (CTIT) University of Twente, Tech. Rep (2005)
2005
Earlier work this paper cites.
Helmert, M.: The fast downward planning system. Journal of Artificial Intelligence Research 26
2006
Earlier work this paper cites.
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009)
2009
Earlier work this paper cites.
Sanner, S.: Relational dynamic influence diagram language (RDDL): Language description. Unpublished manuscript. Australian National University 32
2010
Earlier work this paper cites.
Helmert, M., Domshlak, C.: Lm-cut: Optimal planning with the landmark-cut heuristic. Seventh international planning competition (IPC 2011), deterministic part, 103–105 (2011)
2011
Earlier work this paper cites.
Keller, T., Eyerich, P.: PROST: Probabilistic planning based on UCT. In: Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling (ICAPS) (2012)
2012
Earlier work this paper cites.
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: International Conference on Learning Representations (2013)
2013
Earlier work this paper cites.
Keller, T., Helmert, M.: Trial-based heuristic tree search for finite horizon MDPs. In: Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling (ICAPS) (2013)
2013
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al
2015
Earlier work this paper cites.
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning (2016)
2016
Earlier work this paper cites.
Pevný, T., Somol, P.: Discriminative models for multi-instance problems with tree structure. In: Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, pp. 83–91 (2016). ACM
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
2016
Earlier work this paper cites.
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. In: International Conference on Learning Representations (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep Sets. In: Advances in Neural Information Processing Systems, pp. 3391–3401 (2017)
2017
Cited alongside, same era.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
2017
Cited alongside, same era.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2017)
2017
Cited alongside, same era.
Racanière, S., Weber, T., Reichert, D., Buesing, L., Guez, A., Rezende, D.J., Badia, A.P., Vinyals, O., Heess, N., Li, Y., et al
Groshev, E., Tamar, A., Goldstein, M., Srivastava, S., Abbeel, P.: Learning generalized reactive policies using deep neural networks. In: 2018 AAAI Spring Symposium Series (2018)
2018
Later among the works it cites.
Toyer, S., Trevizan, F., Thiébaux, S., Xie, L.: Action schema networks: Generalised policies with deep learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Later among the works it cites.
Jaderberg, M., Czarnecki, W.M., Dunning, I., Marris, L., Lever, G., Castaneda, A.G., Beattie, C., Rabinowitz, N.C., Morcos, A.S., Ruderman, A., et al
2019
Later among the works it cites.
Guez, A., Mirza, M., Gregor, K., Kabra, R., Racanière, S., Weber, T., Raposo, D., Santoro, A., Orseau, L., Eccles, T., et al
2019
Later among the works it cites.
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alphaXiv is searching for related work…
2017
Cited alongside, same era.
Santoro, A., Raposo, D., Barrett, D.G., Malinowski, M., Pascanu, R., Battaglia, P., Lillicrap, T.: A simple neural network module for relational reasoning. In: Advances in Neural Information Processing Systems, pp. 4967–4976 (2017)
2017
Cited alongside, same era.
Baader, F., Horrocks, I., Lutz, C., Sattler, U.: Introduction to Description Logic. Cambridge University Press, Cambridge (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Li, Y.: Deep reinforcement learning. arXiv preprint arXiv:1810.06339 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Harmer, J., Gisslén, L., Val, J., Holst, H., Bergdahl, J., Olsson, T., Sjöö, K., Nordin, M.: Imitation learning with concurrent actions in 3D games. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8 (2018). IEEE
2018
Cited alongside, same era.
2019
Later among the works it cites.
Bapst, V., Sanchez-Gonzalez, A., Doersch, C., Stachenfeld, K., Kohli, P., Battaglia, P., Hamrick, J.: Structured agents for physical construction. In: International Conference on Machine Learning, pp. 464–474 (2019). PMLR
2019
Later among the works it cites.
Zambaldi, V., Raposo, D., Santoro, A., Bapst, V., Li, Y., Babuschkin, I., Tuyls, K., Reichert, D., Lillicrap, T., Lockhart, E., et al
2019
Later among the works it cites.
2019
Later among the works it cites.
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1
2020
Closest in time.
2020
Closest in time.
Garg, S., Bajpai, A., Mausam: Symbolic network: generalized neural policies for relational mdps. In: International Conference on Machine Learning, pp. 3397–3407 (2020). PMLR
2020
Closest in time.
Toyer, S., Thiébaux, S., Trevizan, F., Xie, L.: Asnets: Deep learning for generalised planning. Journal of Artificial Intelligence Research 68
2020
Closest in time.
Shen, W., Trevizan, F., Thiébaux, S.: Learning domain-independent planning heuristics with hypergraph networks. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 30, pp. 574–584 (2020)
2020
Closest in time.
2020
Closest in time.
Ng, J.H.A., Petrick, R.: Firstorder function approximation for transfer learning in relational mdps. In: ICAPS PRL Workshop (2021)
2021
Closest in time.
Frances, G., Bonet, B., Geffner, H.: Learning general planning policies from small examples without supervision. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11801–11808 (2021)
2021
Closest in time.
Chester, A., Dann, M., Zambetta, F., Thangarajah, J.: Oracle-sage: Planning ahead in graph-based deep reinforcement learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 52–67 (2022). Springer
2022
Closest in time.
Karia, R., Srivastava, S.: Relational abstractions for generalized reinforcement learning on symbolic problems. In: 31st International Joint Conference on Artificial Intelligence, IJCAI 2022, pp. 3135–3142 (2022). International Joint Conferences on Artificial Intelligence
2022
Closest in time.