Planning Sequential Interventions to tackle Depression in Large Uncertain Social Networks using Deep Reinforcement Learning

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Planning Sequential Interventions to tackle Depression in Large Uncertain Social Networks using Deep Reinforcement Learning
Title:
Planning Sequential Interventions to tackle Depression in Large Uncertain Social Networks using Deep Reinforcement Learning
Other Titles:
Neurocomputing
Publication Date:
19 January 2022
Citation:
Phyu Aung, A. P., Jayavelu, S., Li, X., & An, B. (2022). Planning Sequential Interventions to tackle Depression in Large Uncertain Social Networks using Deep Reinforcement Learning. Neurocomputing. doi:10.1016/j.neucom.2022.01.030
Abstract:
Studies, with the increasing concern for mental health, have shown that interventions along with social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Meanwhile, Deep Reinforcement Learning (DRL) has shown significant success in learning an efficient policy for sequential decision-making problems in both fully observable environments and partially observable environments with small action space. In this paper, we consider emotion propagation from other neighbours of the influencees, initial uncertainties of mental states and influence in the student network. We propose a new architecture called DRLPSO (Deep Reinforcement Learning with Particle Swarm Optimization) to enhance learning performance in a partially observable environment with a large state and action space. DRLPSO consists of two stages: the Discrete Particle Swarm Optimization (DPSO) and Deep Q-learning integrated with Long Short-Term Memory (DQ-LSTM). In the first stage, we apply DPSO by initializing n particles that converge to multiple optimal actions for each belief state. In the second stage, the action with the best Q-value from the DPSO action set is executed to obtain belief and observation (history of action). We evaluated the proposed method empirically with the simulated student networks with mental state propagation compared to the state-of-the-art algorithms. The experimental results demonstrate that DRLPSO outperforms the state-of-the-art DRL methods by an average of 32%.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
There was no specific funding for the research done
Description:
ISSN:
0925-2312
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