suboptimality
TowardtheFundamentalLimitsofImitation Learning
We then propose a novel algorithm based on minimum-distance functionals in the setting where the transition model is given and the expert is deterministic.Thealgorithmissuboptimalby .|S|H3/2/N,matchingourlower
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Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
In online advertising, for instance, the user's profile is the context, the
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Super Hard
We thank all the reviewers for their feedback. All reviewers are concerned whether we substantially outperform QMIX. Since StarCraft II experiments take a long time, we could not include all the results in the submission. Samvelyan et al. have classified as Easy, Hard & Super Hard. Results on several maps are shown below.
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Corruption-Robust Offline Reinforcement Learning with General Function Approximation
These authors contributed equally to this work.
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