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Found 5 papers, 0 papers with code
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition
Instead of making behavioral decisions directly from the exponentially expanding joint observational-action space, subtask-based multi-agent reinforcement learning (MARL) methods enable agents to learn how to tackle different subtasks.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+4
Learning to Retrieve for Job Matching
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking.
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
This methodology decouples the training of the GNN model from that of existing Deep Neural Nets (DNN) models, eliminating the need for frequent GNN retraining while maintaining up-to-date graph signals in near realtime, allowing for the effective integration of GNN insights through transfer learning.
Optimization of Image Transmission in a Cooperative Semantic Communication Networks
In this paper, a semantic communication framework for image transmission is developed.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning
The CPM identifies non-self positives in a contextual queue to boost learning.
