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Research map / Multi-agent systems and games / Human-AI and goal-conditioned

CompILE: Compositional Imitation Learning and Execution

ICML 2019 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Imitation and skill learning · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use unsupervised CompILE segmentation instead of LSTM surprisalReacher boundary prediction · Reacher, 3 tasksaccuracy: 24.8 \pm 0.6 -> 62.0 \pm 4.5Helped
use unsupervised CompILE segmentation instead of LSTM surprisalReacher boundary prediction · Reacher, 5 tasksaccuracy: 21.6 \pm 0.5 -> 41.7 \pm 8.0Helped
add supervision on segment encodings zCompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 99.5 \pm 0.2Helped
add supervision on boundary positionsCompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 99.8 \pm 0.1Helped
add supervision on segment encodings zCompILE · Reacher, 5 tasksaccuracy: 41.7 \pm 8.0 -> 98.4 \pm 0.5Helped
add supervision on boundary positionsCompILE · Reacher, 5 tasksaccuracy: 41.7 \pm 8.0 -> 98.8 \pm 0.3Helped
replace discrete codes with 32-dimensional Gaussian latent variables (ablation)CompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 45.2 \pm 13.8Hurt
replace discrete codes with 32-dimensional Gaussian latent variables (ablation)CompILE · Reacher, 5 tasksaccuracy: 41.7 \pm 8.0 -> 28.7 \pm 7.0Hurt
increase the allowed number of segments from 3 to 4 (ablation)CompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 53.4 \pm 6.3Hurt
increase the allowed number of segments from 3 to 5 (ablation)CompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 29.8 \pm 6.1Hurt
lower the fixed Gumbel softmax temperature to 0.1 (ablation)CompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 40.9 \pm 5.3Hurt
lower the fixed Gumbel softmax temperature to 0.01 (ablation)CompILE · Reacher, 3 tasksaccuracy: 62.0 \pm 4.5 -> 36.3 \pm 6.0Hurt

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