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One of the most successful paradigms for reward learning uses human feedbackin the form of comparisons. Although these methods hold promise, humancomparison labeling is expensive and time consuming, constituting a majorbottleneck to their broader applicability. Our insight is that we can greatlyimprove how effectively human time is used in these approaches by batchingcomparisons together, rather than having the human label each comparisonindividually. To do so, we leverage data dimensionality-reduction andvisualization techniques to provide the human with a interactive GUI displayingthe state space, in which the user can label subportions of the state space.Across some simple Mujoco tasks, we show that this high-level approach holdspromise and is able to greatly increase the performance of the resultingagents, provided the same amount of human labeling time.

Time-Efficient Reward Learning via Visually Assisted Cluster Ranking