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Expert Data Augmentation in Imitation Learning (Student Abstract)

AAAI 2023 · Reinforcement learning › Imitation learning · approach: Behavior cloning theory and models · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · MsPacmanreturn: 207.6±4.8 -> 1061.2±177.2Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · Breakoutreturn: 0.9±0.8 -> 17.0±9.1Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · SpaceInvadersreturn: 175±58.5 -> 218.6±100.7Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · Qbertreturn: 0±0 -> 770.5±350.4Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · Hopperreturn: 94.9±28.9 -> 1722.4±278.7Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · Antreturn: 521.8±157.1 -> 931.8±477.6Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · Walker2dreturn: 165.9±43.3 -> 422.3±319.6Helped
Augment one expert trajectory with sampled transitions that return to expert-like statesBC · HalfCheetahreturn: -174.3±464.1 -> 43.7±102.9Helped

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