A simulation-based study claims that combining per-service reinforcement learning agents with graph embeddings and an evolutionary strategy-selection step improves coordination and adaptation metrics in microservice systems.
Dynamic operating system scheduling using double DQN: A reinforcement learning approach to task optimization,
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Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems
A simulation-based study claims that combining per-service reinforcement learning agents with graph embeddings and an evolutionary strategy-selection step improves coordination and adaptation metrics in microservice systems.