13 citations · 26 across the 9 of their papers we have counts for
4 papers · 1 filter
Provably Efficient Reinforcement Learning via Surprise Bound
Hanlin Zhu, Ruosong Wang, Jason D. Lee
Value function approximation is important in modern reinforcement learning (RL) problems especially when the state space is (infinitely) large. Despite the importance and wide appl…
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Hanlin Zhu, Amy Zhang
Goal-conditioned reinforcement learning (GCRL) refers to learning general-purpose skills that aim to reach diverse goals. In particular, offline GCRL only requires purely pre-colle…
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient d…
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Paria Rashidinejad, Hanlin Zhu, Kunhe Yang +2
Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years. Mu…