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Poster
Deep Reinforcement Learning of Marked Temporal Point Processes
Utkarsh Upadhyay · Abir De · Manuel Gomez Rodriguez

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #131
in Wed Poster Session B »

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 stochastic discrete events characterized using marked temporal point processes. In doing so, we define the agent's policy using the intensity and mark distribution of the corresponding process and then derive a flexible policy gradient method, which embeds the agent's actions and the feedback it receives into real-valued vectors using deep recurrent neural networks. Our method does not make any assumptions on the functional form of the intensity and mark distribution of the feedback and it allows for arbitrarily complex reward functions. We apply our methodology to two different applications in viral marketing and personalized teaching and, using data gathered from Twitter and Duolingo, we show that it may be able to find interventions to help marketers and learners achieve their goals more effectively than alternatives.

Keywords: Time Series Analysis · Reinforcement Learning

Author Information

Utkarsh Upadhyay (Max Plank Institute for Software Systems)

Bringing clarity to public discourse.

Abir De (Max Planck Insitute for Software Systems)
Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)

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