Changjie Fan
7 papers in the PaperMetrix corpus
Papers by this author
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Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning
2020
Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the …
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Multi-source Data Multi-task Learning for Profiling Players in Online Games
2020 · 2020 IEEE Conference on Games (CoG)
Profiling game players, especially potential churn and payment prediction, is of paramount importance for online games to improve the product design and the revenue. However, current solutions view either churn or payment prediction as an …
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Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
2021 · arXiv (Cornell University)
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle …
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KuiLeiXi: a Chinese Open-Ended Text Adventure Game
2021
Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu. Proceedings of the …
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An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
2020 · arXiv (Cornell University)
Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can share knowledge with each other. However, it remains a problem of …
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RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
2021 · arXiv (Cornell University)
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large …
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EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
2022 · arXiv (Cornell University)
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on …