AI Research
Language Model Augmented Decision Transformer for Multi-Task Offline Reinforcement Learning
A language-model-augmented approach to learning across heterogeneous offline tasks
Offline multi-task reinforcement learning (MTRL) aims to learn a unified policy from heterogeneous offline datasets that generalizes across tasks.
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ArXiv CS.CL
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ArXiv CS.CL
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Learning one policy from varied offline datasets could help reinforcement-learning systems transfer across multiple tasks. The title indicates that language models are combined with a Decision Transformer, though the supplied text does not describe results.
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