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Found 7 papers, 2 papers with code
Multi-agent Reinforcement Learning: A Comprehensive Survey
The prevalence of multi-agent applications pervades various interconnected systems in our everyday lives.
Decentralized Multi-agent Filtering
This paper addresses the considerations that comes along with adopting decentralized communication for multi-agent localization applications in discrete state spaces.
Mix and Mask Actor-Critic Methods
These mechanisms behaves dynamically to couple and decouple connected latent features variably between the policy and value function, while the distributional scalarization standardizes the two objectives using a probabilistic standpoint.
Synthetic Embedding-based Data Generation Methods for Student Performance
Given the inherent class imbalance issue within student performance datasets, samples belonging to the edges of the target class distribution pose a challenge for predictive machine learning algorithms to learn.
Hierarchical BiGraph Neural Network as Recommendation Systems
Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain.
Greedy Bandits with Sampled Context
Bayesian strategies for contextual bandits have proved promising in single-state reinforcement learning tasks by modeling uncertainty using context information from the environment.
Generative Multi-Stream Architecture For American Sign Language Recognition
With advancements in deep model architectures, tasks in computer vision can reach optimal convergence provided proper data preprocessing and model parameter initialization.
