EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning

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EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda,1Katherine Driggs-Campbell,2and Mykel J. Kochenderfer1 Abstract ? Although imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which attempts to quantify the con?dence of the novice policy as a proxy for safety. Our method, EnsembleDAgger, approximates a Gaussian Process using an ensemble of neural networks. Using the variance as a measure of con?dence, we compute a decision rule that captures how much we doubt the novice, thus determining when it is safe to allow the novice to act. With this approach, we aim to maximize the novice?s share of actions, while constraining the probability of failure. We demonstrate improved ...

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• [1] https://arxiv.org/abs/1807.08364