AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-2022)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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MoralityGym is a new benchmark using 98 ethical dilemmas in sequential environments to evaluate hierarchical moral alignment in AI agents via Morality Chains and a Morality Metric.
citing papers explorer
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AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
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MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents
MoralityGym is a new benchmark using 98 ethical dilemmas in sequential environments to evaluate hierarchical moral alignment in AI agents via Morality Chains and a Morality Metric.