Corpus ID: 43934039

Deep Reinforcement Learning of Marked Temporal Point Processes

@article{Upadhyay2018DeepRL,
  title={Deep Reinforcement Learning of Marked Temporal Point Processes},
  author={U. Upadhyay and A. De and M. Gomez-Rodriguez},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.09360}
}

U. Upadhyay, A. De, M. Gomez-Rodriguez

Published 2018

Computer Science, Mathematics

ArXiv

In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online interventions that will help humans achieve certain goals in such asynchronous setting? In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous… CONTINUE READING

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