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Found 8 papers, 0 papers with code
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Often there exists a hierarchical structure of intent fulfilment where multiple pre-trained, self-interested agents may need to be further orchestrated by a supervisor or controller agent.
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity
The dynamic and evolutionary nature of service requirements in wireless networks has motivated the telecom industry to consider intelligent self-adapting Reinforcement Learning (RL) agents for controlling the growing portfolio of network services.
Multi-agent reinforcement learning for intent-based service assurance in cellular networks
Recently, intent-based management has received good attention in telecom networks owing to stringent performance requirements for many of the use cases.
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning
Multi-Agent reinforcement learning has received lot of attention in recent years and have applications in many different areas.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Zero-Shot Federated Learning with New Classes for Audio Classification
Federated learning is an effective way of extracting insights from different user devices while preserving the privacy of users.
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring
Such applications demand characterization of insights from multiple resource-constrained user devices using machine learning techniques for effective personalized activity monitoring.
Resource-Constrained Federated Learning with Heterogeneous Labels and Models
In addition, in this paper we explore a new challenge of interest -- to handle label heterogeneities in federated learning.
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting
Due to this limitation, approaches using a static set of weights for weighing ensemble models cannot capture the dynamic changes or local features of the data effectively.
