4 citations · 4 across the 4 of their papers we have counts for
5 papers
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data
Zeyu Jia, Alexander Rakhlin, Ayush Sekhari +1
We revisit the problem of offline reinforcement learning with value function realizability but without Bellman completeness. Previous work by Xie and Jiang (2021) and Foster et al.…
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
Yifei Zhou, Ayush Sekhari, Yuda Song +1
Hybrid RL is the setting where an RL agent has access to both offline data and online data by interacting with the real-world environment. In this work, we propose a new hybrid RL…
When is Agnostic Reinforcement Learning Statistically Tractable?
Zeyu Jia, Gene Li, Alexander Rakhlin +2
We study the problem of agnostic PAC reinforcement learning (RL): given a policy class , how many rounds of interaction with an unknown MDP (with a potentially large state and a…
Contextual Bandits and Imitation Learning via Preference-Based Active Queries
Ayush Sekhari, Karthik Sridharan, Wen Sun +1
We consider the problem of contextual bandits and imitation learning, where the learner lacks direct knowledge of the executed action's reward. Instead, the learner can actively qu…
On the Complexity of Adversarial Decision Making
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari +1
A central problem in online learning and decision making -- from bandits to reinforcement learning -- is to understand what modeling assumptions lead to sample-efficient learning g…