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Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator
We propose a new method for training an agent via an evolutionary strategy (ES), in which we iteratively improve a set of samples to imitate: Starting with a random set, in every iteration we replace a subset of the samples with samples from the best trajectories discovered so far.
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