
Exploratory Combinatorial Optimization with Reinforcement Learning
Many realworld problems can be reduced to combinatorial optimization on...
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Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
In this work, we introduce Graph Pointer Networks (GPNs) trained using r...
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Reinforcement Learning with Chromatic Networks
We present a new algorithm for finding compact neural networks encoding ...
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Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
Combinatorial optimization problem (COP) over graphs is a fundamental ch...
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On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization
Combinatorial optimization problems (COPs) on the graph with reallife a...
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How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
We consider the problem of monitoring and controlling a partiallyobserv...
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Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection
Classical methods for psychometric function estimation either require ex...
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Active Screening for Recurrent Diseases: A Reinforcement Learning Approach
Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically select a subset of population for screening. However, given the limited number of health workers, only a small subset of the population can be visited in any given time period. Given the recurrent nature of the disease and rapid spreading, the goal is to minimize the number of infections over a long time horizon. Active screening can be formalized as a sequential combinatorial optimization over the network of people and their connections. The main computational challenges in this formalization arise from i) the combinatorial nature of the problem, ii) the need of sequential planning and iii) the uncertainties in the infectiousness states of the population. Previous works on active screening fail to scale to large time horizon while fully considering the future effect of current interventions. In this paper, we propose a novel reinforcement learning (RL) approach based on Deep QNetworks (DQN), with several innovative adaptations that are designed to address the above challenges. First, we use graph convolutional networks (GCNs) to represent the Qfunction that exploit the node correlations of the underlying contact network. Second, to avoid solving a combinatorial optimization problem in each time period, we decompose the node set selection as a subsequence of decisions, and further design a twolevel RL framework that solves the problem in a hierarchical way. Finally, to speedup the slow convergence of RL which arises from reward sparseness, we incorporate ideas from curriculum learning into our hierarchical RL approach. We evaluate our RL algorithm on several realworld networks.
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