Deep Reinforcement Learning of Marked Temporal Point Processes

TL;DR

This paper introduces a deep reinforcement learning framework for marked temporal point processes, enabling online interventions in asynchronous environments with complex feedback, demonstrated through personalized teaching and viral marketing applications.

Contribution

It develops a novel policy gradient method that models actions and feedback as marked temporal point processes using deep neural networks, without assuming specific functional forms.

Findings

Outperforms existing methods in personalized teaching scenarios.

Effective in viral marketing applications with real-world data.

Flexible approach handling complex reward functions.

Abstract

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 personalized teaching and viral marketing and, using data gathered from Duolingo and Twitter, we show that it may be able to find interventions to help learners and marketers achieve their goals more effectively than alternatives.