A low-rank matrix estimation method in a reward-free RL framework learns shared representations across linear MDPs and yields near-optimal policies with characterized regret bounds under relaxed feature assumptions.
Provably efficient multi-task meta ban- dit learning via shared representations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
contradiction 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1roles
contradiction 1polarities
contest 1representative citing papers
citing papers explorer
-
Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
A low-rank matrix estimation method in a reward-free RL framework learns shared representations across linear MDPs and yields near-optimal policies with characterized regret bounds under relaxed feature assumptions.