Introduces a theoretical framework for multi-task reinforcement learning with provable guarantees using low-rank reward structures. Applies representation learning techniques to enable principled transfer across related RL tasks while maintaining computational tractability.
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Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
New theoretical framework proves multi-task RL can leverage low-rank reward structures for provably-efficient transfer learning across related tasks.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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