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Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks
In recent years, there has been some work on AI-based IMFs that can handle conflicting intents and prioritize the global objective based on apriori definition of the utility function and accorded priorities for competing intents.
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: Coordinated look-ahead strategy in stochastic 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
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
