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Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i.e., data efficiency, lack of the interpretability and transferability.
Ghallab, M., Knoblock, C., Wilkins, D., Barrett, A., Christianson, D., Friedman, M., Kwok, C., Golden, K., Penberthy, S., Smith, D., Sun, Y., Weld, D., 08 1998. Pddl - the planning domain definition language
1998
Earlier work this paper cites.
Sutton, R. S., Precup, D., Singh, S. P., 1999. Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artif. Intell. 112 (1-2), 181–211
1999
Earlier work this paper cites.
2001
Earlier work this paper cites.
Hoffmann, J., 2002. Extending FF to numerical state variables. In: van Harmelen, F. (Ed.), Proceedings of the 15th Eureopean Conference on Artificial Intelligence, ECAI’2002, Lyon, France, Jusly 2002. IOS Press, pp. 571–575
2002
Earlier work this paper cites.
Ryan, M. R. K., 2002. Using abstract models of behaviours to automatically generate reinforcement learning hierarchies. In: Sammut, C., Hoffmann, A. G. (Eds.), Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002. Morgan Kaufmann, pp. 522–529
2002
Earlier work this paper cites.
Yang, Q., Wu, K., Jiang, Y., 2007. Learning action models from plan examples using weighted MAX-SAT. Artif. Intell. 171 (2-3), 107–143
2006
Earlier work this paper cites.
Zhuo, H. H., Hu, D. H., Hogg, C., Yang, Q., Muñoz-Avila, H., 2009. Learning HTN method preconditions and action models from partial observations. In: Boutilier, C. (Ed.), IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009. pp. 1804–1810
2009
Earlier work this paper cites.
Zhuo, H. H., Yang, Q., Hu, D. H., Li, L., 2010. Learning complex action models with quantifiers and logical implications. Artif. Intell. 174 (18), 1540–1569
2010
Earlier work this paper cites.
Zhuo, H. H., Yang, Q., Pan, R., Li, L., 2011. Cross-domain action-model acquisition for planning via web search. In: Bacchus, F., Domshlak, C., Edelkamp, S., Helmert, M. (Eds.), Proceedings of the 21st International Conference on Automated Planning and Scheduling, ICAPS 2011, Freiburg, Germany June 11-16, 2011. AAAI
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
Zhuo, H. H., Kambhampati, S., 2013. Action-model acquisition from noisy plan traces. In: Rossi, F. (Ed.), IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013. IJCAI/AAAI, pp. 2444–2450
2013
Earlier work this paper cites.
Zhuo, H. H., Munoz-Avila, H., Yang, Q., 2014. Learning hierarchical task network domains from partially observed plan traces. Artif. Intell. 212, 134–157
2014
Earlier work this paper cites.
Zhuo, H. H., Yang, Q., 2014. Action-model acquisition for planning via transfer learning. Artif. Intell. 212, 80–103
2014
Cited alongside, same era.
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. Human-level control through deep reinforcement learning. nature 518 (7540), 529–533
2015
Cited alongside, same era.
Zhuo, H. H., 2015. Crowdsourced action-model acquisition for planning. In: AAAI. pp. 3439–3446
2015
Cited alongside, same era.
Kulkarni, T. D., Narasimhan, K., Saeedi, A., Tenenbaum, J., 2016. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. In: Lee, D. D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. pp. 3675–3683
2016
Cited alongside, same era.
Yang, F., Lyu, D., Liu, B., Gustafson, S., 2018. PEORL: integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. In: Lang, J. (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden. ijcai.org, pp. 4860–4866
2018
Later among the works it cites.
Gerevini, A. E., 2020. An introduction to the planning domain definition language (PDDL): book review. Artif. Intell. 280, 103221
2019
Later among the works it cites.
Lyu, D., Yang, F., Liu, B., Gustafson, S., 2019. SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, pp. 2970–2977
2019
Later among the works it cites.
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Leonetti, M., Iocchi, L., Stone, P., 2016. A synthesis of automated planning and reinforcement learning for efficient, robust decision-making. Artif. Intell. 241, 103–130
2016
Cited alongside, same era.
Martínez, D., Alenyà, G., Torras, C., Ribeiro, T., Inoue, K., 2016. Learning relational dynamics of stochastic domains for planning. In: Coles, A. J., Coles, A., Edelkamp, S., Magazzeni, D., Sanner, S. (Eds.), Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, ICAPS 2016, London, UK, June 12-17, 2016. AAAI Press, pp. 235–243
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T. P., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D., 2016. Mastering the game of go with deep neural networks and tree search. Nat. 529 (7587), 484–489
2016
Cited alongside, same era.
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al., 2017. Mastering the game of go without human knowledge. Nature 550 (7676), 354–359
2017
Cited alongside, same era.
Zhou, Z., Li, X., Zare, R. N., 2017. Optimizing chemical reactions with deep reinforcement learning. ACS central science 3 (12), 1337–1344
2017
Cited alongside, same era.
Zhuo, H. H., Kambhampati, S., 2017. Model-lite planning: Case-based vs. model-based approaches. Artif. Intell. 246, 1–21
2017
Cited alongside, same era.
Icarte, R. T., Klassen, T. Q., Valenzano, R. A., McIlraith, S. A., 2018. Advice-based exploration in model-based reinforcement learning. In: Bagheri, E., Cheung, J. C. K. (Eds.), Advances in Artificial Intelligence - 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8-11, 2018, Proceedings. Vol. 10832 of Lecture Notes in Computer Science. Springer, pp. 72–83
2018
Cited alongside, same era.
Ng, J. H. A., Petrick, R. P. A., 2019. Incremental learning of planning actions in model-based reinforcement learning. In: Kraus, S. (Ed.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019. ijcai.org, pp. 3195–3201
2019
Later among the works it cites.
Illanes, L., Yan, X., Icarte, R. T., McIlraith, S. A., 2020. Symbolic plans as high-level instructions for reinforcement learning. In: Beck, J. C., Buffet, O., Hoffmann, J., Karpas, E., Sohrabi, S. (Eds.), Proceedings of ICAPS. AAAI Press, pp. 540–550
2020
Later among the works it cites.
James, S., Rosman, B., Konidaris, G., 2020. Learning portable representations for high-level planning. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event. Vol. 119 of Proceedings of Machine Learning Research. PMLR, pp. 4682–4691
2020
Later among the works it cites.
Shen, J., Zhuo, H. H., Xu, J., Zhong, B., Pan, S. J., 2020. Transfer value iteration networks. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. AAAI Press, pp. 5676–5683
2020
Later among the works it cites.
Xiao, Z., Wan, H., Zhuo, H. H., Herzig, A., Perrussel, L., Chen, P., 2020. Refining HTN methods via task insertion with preferences. In: AAAI. AAAI Press, pp. 10009–10016
2020
Later among the works it cites.
Lee, J., Katz, M., Agravante, D. J., Liu, M., Klinger, T., Campbell, M., Sohrabi, S., Tesauro, G., 2021. Ai planning annotation in reinforcement learning: Options and beyond. In: Planning and Reinforcement Learning PRL Workshop at ICAPS
2021
Closest in time.
Jin, M., Ma, Z., Jin, K., Zhuo, H. H., Chen, C., Yu, C., 2022. Creativity of AI: automatic symbolic option discovery for facilitating deep reinforcement learning. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022. AAAI Press, pp. 7042–7050
2022
Closest in time.
van Hasselt, H., Guez, A., Silver, D., 2016. Deep reinforcement learning with double q-learning. In: Schuurmans, D., Wellman, M. P. (Eds.), Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA. AAAI Press, pp. 2094–2100
2094
Closest in time.