Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
Multi-task representation learning (MTRL) is an approach that learns shared latent representations across related tasks, facilitating collaborative learning that improves the overall learning efficiency. This paper studies MTRL for multi-task reinforcement learning (RL), where multiple tasks have the same state-action space and transition probabilities, but different rewards. We consider T linear Markov Decision Processes (MDPs) where the reward functions and transition dynamics admit linear feature embeddings of dimension d. The relatedness among the tasks is captured by a low-rank structure on the reward matrices. Learning shared representations across multiple RL tasks is challenging d...
| Authors | Yaoze Guo, Shana Moothedath |
|---|---|
| Primary category | cs.LG |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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