Task-Relevant Adversarial Imitation Learning

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We showed through ablations that Task-Relevant Adversarial Imitation Learning benefits from data augmentation, actor early stopping, and the use of invariant set constraints

Abstract:

We show that a critical problem in adversarial imitation from high-dimensional sensory data is the tendency of discriminator networks to distinguish agent and expert behaviour using task-irrelevant features beyond the control of the agent. We analyze this problem in detail and propose a solution as well as several baselines that outperf...More

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