[31] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning. PMLR, 1928-1937.
[32] German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019. Continual lifelong learning with neural networks: A review. Neural networks 113 (2019), 54-71.
[33] Sung-Jun Park, Dong-Kyu Chae, Hong-Kyun Bae, Sumin Park, and Sang-Wook Kim. 2022. Reinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 784-793.
[34] Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell. 2017. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning. PMLR, 2778-2787.
[35] Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, and Yongfeng Zhang. 2019. Value-aware recommendation based on reinforcement profit maximization. In The World Wide Web Conference. 3123-3129.
[36] Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al. 2019. Personalized re-ranking for recommendation. In Proceedings of the 13th ACM conference on recommender systems. 3-11.
[37] Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, and Mohi Khansari. 2020. Rl-cyclegan: Reinforcement learning aware simulation-to-real. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11157-11166.
[38] David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou. 2018. Recogym: A reinforcement learning environment for the problem of product recommendation in online advertising. arXiv preprint arXiv:1808.00720 (2018).
[39] Reuven Y Rubinstein and Dirk P Kroese. 2004. The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning. Vol. 133. Springer.
[40] Erica Salvato, Gianfranco Fenu, Eric Medvet, and Felice Andrea Pellegrino. 2021. Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning. IEEE Access 9 (2021), 153171-153187.
[41] Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and An-Xiang Zeng. 2019. Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4902-4909.
[42] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441-1450.
[43] Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining. 565-573.
[44] Kai Wang, Zhene Zou, Yue Shang, Qilin Deng, Minghao Zhao, Yile Liang, Runze Wu, Jianrong Tao, Xudong Shen, Tangjie Lyu, et al. 2021. RL4rs: A real-world benchmark for reinforcement learning based recommender system. arXiv preprint arXiv:2110.11073 (2021).
[45] Lu Wang, Wei Zhang, Xiaofeng He, and Hongyuan Zha. 2018. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2447-2456.
[46] Shoujin Wang, Longbing Cao, Yan Wang, Quan Z Sheng, Mehmet A Orgun, and Defu Lian. 2021. A survey on session-based recommender systems. ACM Computing Surveys (CSUR) 54, 7 (2021), 1-38.