49 citations · 49 across the 2 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2020
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
Priyank Agrawal, Jinglin Chen, Nan Jiang
This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping varia…
cs.LG2019★ 49 cited
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang
Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucia…