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Deep Sets for Generalization in RL

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Academic year: 2021

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Figure

Figure 1: Modular architectures with attention. Left: policy. Right: reward function.
Figure 2: Reward function and policy learning. a: Train- Train-ing (left) and testTrain-ing (right) performances of the reward function after convergence (stars indicate significant differ-ences w.r.t
Figure 5: Representation of possible ob- ob-jects types and categories.
Figure 6: Data distributions for the supervised learning of the reward function. Sorted counts of positive examples per training set descriptions.
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