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Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning

ICLR 2017 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Intrinsically motivated exploration · 106 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use invariant feature-space transfer learned from shared proxy skills rather than learning from scratch4-link arm · Button pressingsuccess rateHelped
Use learned invariant feature-space transfer rather than learning from scratchTendon-driven arm · Block pullingtask performanceHelped
Use learned invariant feature-space transfer from raw images rather than learning from scratch4-link arm · Block pushingtask performanceHelped
Use deep neural-network embeddings instead of CCAInvariant feature-space transfer · Button pressinglearning speedHelped
Use a direct source-to-target state mapping instead of a shared feature spaceInvariant feature-space transfer · Button pressingtask performanceHurt
Use KCCA with a quadratic kernel instead of the learned neural embeddingTransfer method · Button pressingtask performanceHurt
Use KCCA with a quadratic kernel instead of the learned neural embeddingTransfer method · Block pullingtask performanceNo clear effect
Learn embeddings from all three proxy tasks instead of one proxy task (ablation)Invariant feature-space transfer · Button pressingsuccess rateHelped
Use peg insertion as the sole proxy task instead of all three proxy tasks (ablation)Invariant feature-space transfer · Button pressingsuccess rateHurt
Use block moving as the sole proxy task instead of all three proxy tasks (ablation)Invariant feature-space transfer · Button pressingsuccess rateHurt
Use target reaching as the sole proxy task instead of all three proxy tasks (ablation)Invariant feature-space transfer · Button pressingsuccess rateHurt
Estimate correspondences with the EM-style DTW procedure instead of time-based alignment (ablation)Invariant feature-space transfer · Block pullingtask performanceNo clear effect

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