Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations

@article{Peters2021InferringOI,
  title={Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations},
  author={Lasse Peters and David Fridovich-Keil and Vicencc Rubies-Royo and Claire J. Tomlin and C. Stachniss},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.03611}
}
Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theoretical framework for modeling scenarios involving multiple agents with differing objectives interacting over time. A core challenge when formulating a dynamic game is designing objectives for each agent that capture desired behavior. In this paper, we propose a method for inferring parametric objective models of… 

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