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A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes

AAAI 2020 · Multi-agent systems and games › Cooperative multi-agent RL · approach: Coordination graphs · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use VPT instead of API for disease controlGMDP planning · Disease controlrelative deviation d_r [%]: 0 -> 0No clear effect
Use VPT instead of API for forest managementGMDP planning · Forest managementrelative deviation d_r [%]: 1 -> 0 (μ,ν)=(0.3,0.3); 11 -> 1 (μ,ν)=(0.6,0.6); 38 -> 10 (μ,ν)=(0.9,0.9)Helped
Use VPT instead of API for opinion dynamicsGMDP planning · Opinion dynamics95% deviation interval: 5.2 -> 0.8 (μ,ν)=(0.0,0.1); 5.7 -> 1.8 (μ,ν)=(0.1,0.1); 4.7 -> 0.0 (μ,ν)=(0.2,0.2)Helped
Increase policy determinism (higher β)VPT approximate inference · Random reward functions on tree and bidirectional-chain topologiesapproximation accuracyHurt
Use a bidirectional chain rather than a tree topologyVPT approximate inference · Random reward functionsapproximation accuracyHurt
Use VPT rather than mean-field policy in the synchronization task5×5 grid · Agent synchronizationmean order parameter over timeHelped

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