Corpus ID: 203642129

Learning Calibratable Policies using Programmatic Style-Consistency

@article{Zhan2019LearningCP,
  title={Learning Calibratable Policies using Programmatic Style-Consistency},
  author={Eric Zhan and Albert Tseng and Yisong Yue and A. Swaminathan and Matthew J. Hausknecht},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.01179}
}
  • Eric Zhan, Albert Tseng, +2 authors Matthew J. Hausknecht
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • We study the important and challenging problem of controllable generation of long-term sequential behaviors. Solutions to this problem would impact many applications, such as calibrating behaviors of AI agents in games or predicting player trajectories in sports. In contrast to the well-studied areas of controllable generation of images, text, and speech, there are significant challenges that are unique to or exacerbated by generating long-term behaviors: how should we specify the factors of… CONTINUE READING

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