Related Works of Reinforcement Learning (Applied)
Key Words: Reinforcement Learning
Taxonomy
- Applications: Game play / Traffic Light Control / Fleet mangement / Order Dispatch / Bike Reposition / Community Detection / Metro Network Expansion / Paraphrasing / Search Aggregation / User Profiling
- Single-agent / Multi-agent
- Basic algorithms: MDP / DQN / Actor-Critic / Hierarchical RL
- With Simulator / No Simulator
Comments
- Applied track contains paper from KDD, WWW.
- Possible variations:
Individual Papers
- IntelliLight, [KDD-2018-Intellilight]
- Taxonomy: Traffic Light Control,
- Contributions: (1) Real-world data; (2) Policy interpretations; (3) New learning method (phase-gated);
- Efficient Large-Scale Fleet Management, [KDD-2018-Didi-Fleet-Management]
- Taxonomy: Fleet mangement, Multi-agent, DQN & Actor-Critic, With simulator
- Contributions: (1) Proper design of agent, reward and state; (2) Contextual multi-agent RL framework is proposed based on both DQN and Actor-Critic; (3) Simulator developed;
- Order Dispatch in On-Demand Ride-Hailing Platforms, [KDD-2018-Didi-Order-Dispatch]
- Taxonomy: Order Dispatch, Multi-agent, MDP, With simulator
- Contribution: (1) New order dispatch algorithm that optimizes long-term platform efficiency (instant passenger satisfaction + the expected future gain); (2) Problem formulation (one of first work for RL in large-scale real-time systems); (3) Practical issues considered (computational efficiency).
- Dynamic Bike Reposition, [KDD-2018-Bike-Reposition]
- Taxonomy: Bike Reposition, Multi-agent, DQN, With simulator
- Contribution: (1) New clustering algorithm; (2) Simulator; (3) A Spatial-Temporal RL model (new state, DQN); (4) Real-world data;
- Efficient and Effective Express, [KDD-2019-JD]
- Taxonomy: Order Dispatch, Multi-agent ,With Simulator, (Cooperative) DQN
- Contribution: (1) City partition; (2) New clustering algorithm; (3) New RL model (Contextual Cooperative RL); (4) Real-world data;
- Spatio-Temporal Capsule-based RL for Mobility-on-Demand Network Coordination, [WWW-2019-Order-Dispatch]
- Taxonomy: Order Dispatch, Single-agent, No Simulator (?), DQN
- Contribution: (1) ST-RL formulation; (2) ST capsule NN;
- Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical RL, [WWW-2019-Search-Aggregation]
- Taxonomy: Search Aggregation, Hierarchical RL
- Contribution: (1) Hierarchical RL formulation;
- Learning Heuristics for Community Detection with GAN, [KDD-2020-Community-Detection]
- Taxonomy: Community Detection
- City Metro Network Expansion with Reinforcement Learning, [KDD-2020-Metro-Expansion]
- Taxonomy: Metro Network Expansion,
- Contributions: (1) Incorporate social equity concernsl; (2) Formulate metro network expansion as a RL problem; (2) Real-world data;
- Unsupervised Paraphrasing via Deep Reinforcement Learning, [KDD-2020-Paraphrasing]
- Taxonomy: Paraphrasing
- Incremental Mobile User Profiling, [KDD-2020-User-Profiling]
- Taxonomy: User Profiling
- Contributions: (1) New criteria for evaluating user profiling accuracy; (2) RL formulation; (3) RL+Knowledge Grahp; (4) Real-world data; (5) Generalization;
Ref.
[KDD-2018-Intellilight] Wei, Hua, Guanjie Zheng, Huaxiu Yao, and Zhenhui Li. “Intellilight: A reinforcement learning approach for intelligent traffic light control.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496-2505. 2018.
[KDD-2018-Didi-Fleet-Management] Lin, Kaixiang, Renyu Zhao, Zhe Xu, and Jiayu Zhou. “Efficient large-scale fleet management via multi-agent deep reinforcement learning.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1774-1783. 2018.
[KDD-2018-Didi-Order-Dispatch] Xu, Zhe, Zhixin Li, Qingwen Guan, Dingshui Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian, and Jieping Ye. “Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 905-913. 2018.
[KDD-2018-Bike-Reposition] Li, Yexin, Yu Zheng, and Qiang Yang. “Dynamic bike reposition: A spatio-temporal reinforcement learning approach.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1724-1733. 2018.
[KDD-2019-JD] Li, Yexin, Yu Zheng, and Qiang Yang. “Efficient and Effective Express via Contextual Cooperative Reinforcement Learning.” In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 510-519. 2019.
[WWW-2019-Order-Dispatch] He, Suining, and Kang G. Shin. “Spatio-temporal capsule-based reinforcement learning for mobility-on-demand network coordination.” In The World Wide Web Conference, pp. 2806-2813. 2019.
[WWW-2019-Search-Aggregation] Takanobu, Ryuichi, Tao Zhuang, Minlie Huang, Jun Feng, Haihong Tang, and Bo Zheng. “Aggregating e-commerce search results from heterogeneous sources via hierarchical reinforcement learning.” In The World Wide Web Conference, pp. 1771-1781. 2019.
[KDD-2020-Community-Detection] Zhang, Yao, Yun Xiong, Yun Ye, Tengfei Liu, Weiqiang Wang, Yangyong Zhu, and Philip S. Yu. “SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1103-1113. 2020.
[KDD-2020-Metro-Expansion] Wei, Yu, Minjia Mao, Xi Zhao, Jianhua Zou, and Ping An. “City Metro Network Expansion with Reinforcement Learning.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2646-2656. 2020.
[KDD-2020-Paraphrasing] Siddique, A. B., Samet Oymak, and Vagelis Hristidis. “Unsupervised Paraphrasing via Deep Reinforcement Learning.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1800-1809. 2020.
[KDD-2020-User-Profiling] Wang, Pengyang, Kunpeng Liu, Lu Jiang, Xiaolin Li, and Yanjie Fu. “Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 853-861. 2020.