937 citations · 4k across the 76 of their papers we have counts for
8 papers · 1 filter
VOL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
Alekh Agarwal, Yujia Jin, Tong Zhang
We study time-inhomogeneous episodic reinforcement learning (RL) under general function approximation and sparse rewards. We design a new algorithm, Variance-weighted Optimistic $Q…
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy +2
We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard struct…
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
Alekh Agarwal, Tong Zhang
We propose a general framework to design posterior sampling methods for model-based RL. We show that the proposed algorithms can be analyzed by reducing regret to Hellinger distanc…
Provable Benefits of Representational Transfer in Reinforcement Learning
Alekh Agarwal, Yuda Song, Wen Sun +3
We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used…
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling
Alekh Agarwal, Tong Zhang
Provably sample-efficient Reinforcement Learning (RL) with rich observations and function approximation has witnessed tremendous recent progress, particularly when the underlying f…
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift
Alekh Agarwal, Tong Zhang
In this paper, we consider learning scenarios where the learned model is evaluated under an unknown test distribution which potentially differs from the training distribution (i.e.…