255 citations · 2.4k across the 127 of their papers we have counts for
8 papers · 2 filters
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
Philip Amortila, Nan Jiang, Csaba Szepesvári
Theoretical guarantees in reinforcement learning (RL) are known to suffer multiplicative blow-up factors with respect to the misspecification error of function approximation. Yet,…
Context-lumpable stochastic bandits
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori +3
We consider a contextual bandit problem with contexts and actions. In each round , the learner observes a random context and chooses an action based on its pas…
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang +12
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performi…
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
Qinghua Liu, Gellért Weisz, András György +2
While policy optimization algorithms have played an important role in recent empirical success of Reinforcement Learning (RL), the existing theoretical understanding of policy opti…
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
Daniel Kane, Sihan Liu, Shachar Lovett +3
A fundamental question in reinforcement learning theory is: suppose the optimal value functions are linear in given features, can we learn them efficiently? This problem's counterp…
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner +3
A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement…