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.
Reward-free rl is no harder than reward-aware rl in linear markov de- cision processes
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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.