A multimodal fusion emotion recognition method based on multitask learning and attention mechanism

Xie, Jiang; Wang, Jiyu; Wang, Qingyan; Yang, Dechang; Gu, Jinming; Tang, Yongqiang; Varatnitski, Y. I.

doi:10.1016/j.neucom.2023.126649

Cited by 5 publications

(1 citation statement)

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“…This method sets the stage for an enhanced learning trajectory of our attention-based strategies. According to Xie et al (2023) . the algorithmic construct for distributing attention weights emphasizes the amplification of operations pivotal to the core tasks of the model.…”

Section: Research Backgroundmentioning

confidence: 99%

A priority experience replay actor-critic algorithm using self-attention mechanism for strategy optimization of discrete problems

Sun,

Yang

2024

PeerJ Computer Science

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In the dynamic field of deep reinforcement learning, the self-attention mechanism has been increasingly recognized. Nevertheless, its application in discrete problem domains has been relatively limited, presenting complex optimization challenges. This article introduces a pioneering deep reinforcement learning algorithm, termed Attention-based Actor-Critic with Priority Experience Replay (A2CPER). A2CPER combines the strengths of self-attention mechanisms with the Actor-Critic framework and prioritized experience replay to enhance policy formulation for discrete problems. The algorithm’s architecture features dual networks within the Actor-Critic model—the Actor formulates action policies and the Critic evaluates state values to judge the quality of policies. The incorporation of target networks aids in stabilizing network optimization. Moreover, the addition of self-attention mechanisms bolsters the policy network’s capability to focus on critical information, while priority experience replay promotes training stability and reduces correlation among training samples. Empirical experiments on discrete action problems validate A2CPER’s adeptness at policy optimization, marking significant performance improvements across tasks. In summary, A2CPER highlights the viability of self-attention mechanisms in reinforcement learning, presenting a robust framework for discrete problem-solving and potential applicability in complex decision-making scenarios.

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Section: Research Backgroundmentioning

confidence: 99%