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cs.LG2024
Misspecified -Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Ally Yalei Du, Lin F. Yang, Ruosong Wang
The recent work by Dong & Yang (2023) showed for misspecified sparse linear bandits, one can obtain an -optimal policy using a polynomial number of samples when th…
cs.LG2024
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
Junyan Liu, Yunfan Li, Ruosong Wang +1
Existing metrics for reinforcement learning (RL) such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the a…