Knowledge-Enhanced Causal Reinforcement Learning Model for Interactive Recommendation

Nie, Weizhi; Wen, Xin; Liu, Jing; Chen, Jiawei; Wu, Jiancan; Jin, Guoqing; Lu, Jiwen; Liu, An-An

doi:10.1109/tmm.2023.3276505

Cited by 10 publications

(1 citation statement)

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“…The agent's ultimate goal is to learn a policy that effectively maps observations to actions, thereby maximizing expected cumulative rewards over time. In the field of classification and segmentation, researchers have achieved promising outcomes by applying reinforcement learning methods [25]- [30]. For instance, Liao et al [31] proposed a multi-agent strategy for 3D model segmentation, leveraging reinforcement learning principles.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Information Fusion Based on Multi-View and Multi-Feature Learning

Li¹,

Zhang²,

Zhang³

2022

Information Fusion

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With the development of deep learning techniques, supervised learning has achieved performances surpassing those of humans. Researchers have designed numerous corresponding models for different data modalities, achieving excellent results in supervised tasks. However, with the exponential increase of data in multiple fields, the recognition and classification of unlabeled data have gradually become a hot topic. In this paper, we employed a Reinforcement Learning framework to simulate the cognitive processes of humans for effectively addressing novel class discovery in the Open-set domain. We deployed a Member-to-Leader Multi-Agent framework to extract and fuse features from multi-modal information, aiming to acquire a more comprehensive understanding of the feature space. Furthermore, this approach facilitated the incorporation of self-supervised learning to enhance model training. We employed a clustering method with varying constraint conditions, ranging from strict to loose, allowing for the generation of dependable labels for a subset of unlabeled data during the training phase. This iterative process is similar to human exploratory learning of unknown data. These mechanisms collectively update the network parameters based on rewards received from environmental feedback. This process enables effective control over the extent of exploration learning, ensuring the accuracy of learning in unknown data categories. We demonstrate the performance of our approach in both the 3D and 2D domains by employing the OS-MN40, OS-MN40-Miss, and Cifar10 datasets. Our approach achieves competitive competitive results. The source code can be found here: https://github.com/AveryFallson/RMFFN

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Section: Reinforcement Learningmentioning

confidence: 99%