The paper shows that a GNN-based RL controller that reads a graph of the current Pareto front can outperform static tuning and prior RL tuners on multi-objective scheduling problems.
The results, presented in Table 10, compare the best obtained HV values, aligned with the experimental setup of Lin et al
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Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
The paper shows that a GNN-based RL controller that reads a graph of the current Pareto front can outperform static tuning and prior RL tuners on multi-objective scheduling problems.