Search Results for author:
Found 6 papers, 3 papers with code
LLM-based policy generation for intent-based management of applications
And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand.
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data.
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options.
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
On the Robustness of Cooperative Multi-Agent Reinforcement Learning
Our results on the StartCraft II multi-agent benchmark demonstrate that c-MARL teams are highly vulnerable to perturbations applied to one of their agent's observations.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
