229 citations · 664 across the 18 of their papers we have counts for
7 papers · 1 filter
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang, Chi Jin, Zhaoran Wang +2
The classical theory of reinforcement learning (RL) has focused on tabular and linear representations of value functions. Further progress hinges on combining RL with modern functi…
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Qinghua Liu, Tiancheng Yu, Yu Bai +1
Model-based algorithms -- algorithms that explore the environment through building and utilizing an estimated model -- are widely used in reinforcement learning practice and theore…
Near-Optimal Reinforcement Learning with Self-Play
Yu Bai, Chi Jin, Tiancheng Yu
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn the optimal p…
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy +1
Partial observability is a common challenge in many reinforcement learning applications, which requires an agent to maintain memory, infer latent states, and integrate this past in…
On the Theory of Transfer Learning: The Importance of Task Diversity
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
We provide new statistical guarantees for transfer learning via representation learning--when transfer is achieved by learning a feature representation shared across different task…
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz +1
Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To iso…