Two memory-saving variants of LSVI-UCB for linear MDPs are proposed; the fixed-reset variant has a sublinear space-regret trade-off proof, while the adaptive variant lacks a regret guarantee despite the abstract claiming sublinear regret.
Near-optimal regret bounds for reinforcement learning
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Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs
Two memory-saving variants of LSVI-UCB for linear MDPs are proposed; the fixed-reset variant has a sublinear space-regret trade-off proof, while the adaptive variant lacks a regret guarantee despite the abstract claiming sublinear regret.