performance degradation
Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation
Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation.
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On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
Different distribution shifts require different algorithmic and operational interventions. Methodological research must be grounded by the specific shifts they address.
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Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In Reinforcement Learning (RL), an agent engages with an environment in a sequential manner.
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AnoShift: ADistributionShiftBenchmarkfor UnsupervisedAnomalyDetection
Theexisting benchmarks arefocused onsupervised learning, andtothe best of our knowledge, there is none for unsupervised learning.
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