  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.
