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Meta learning for population-based algorithms in black-box optimization

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Academic year: 2021

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Figure 2.1: Overview of a Markov decision process (MDP) where the policy π θ receives rewards
Figure 2.2: Overview of a partially observable Markov decision process (POMDP) where the policy π θ receives rewards (green) due to actions (red) based on the observations (blue)
Figure 2.3: Overview of the learning-to-optimize POMDP (LTO-POMDP). The optimizer π θ
Figure 3.1: The inner-optimization example (left): The algorithm π θ receives the popula-
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