Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.
Unsupervised zero-shot reinforcement learning via functional reward encodings.arXiv preprint arXiv:2402.17135
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Improving Zero-Shot Offline RL via Behavioral Task Sampling
Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.