29 citations · 175 across the 25 of their papers we have counts for
11 papers · 1 filter
Longhorn: State Space Models are Amortized Online Learners
Bo Liu, Rui Wang, Lemeng Wu +3
Modern large language models are built on sequence modeling via next-token prediction. While the Transformer remains the dominant architecture for sequence modeling, its quadratic…
FAMO: Fast Adaptive Multitask Optimization
Bo Liu, Yihao Feng, Peter Stone +1
One of the grand enduring goals of AI is to create generalist agents that can learn multiple different tasks from diverse data via multitask learning (MTL). However, in practice, a…
A Unified Framework for Alternating Offline Model Training and Policy Learning
Shentao Yang, Shujian Zhang, Yihao Feng +1
In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and subsequently utilize the learned model and fixed datase…
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
Bo Liu, Yihao Feng, Qiang Liu +1
Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such…
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
Shentao Yang, Yihao Feng, Shujian Zhang +1
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment dur…
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning
Ziyang Tang, Yihao Feng, Qiang Liu
Reinforcement learning (RL) has drawn increasing interests in recent years due to its tremendous success in various applications. However, standard RL algorithms can only be applie…
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Yihao Feng, Ziyang Tang, Na Zhang +1
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a…
Off-Policy Interval Estimation with Lipschitz Value Iteration
Ziyang Tang, Yihao Feng, Na Zhang +2
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data. When applied to high-stakes scenarios such…
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Yihao Feng, Tongzheng Ren, Ziyang Tang +1
We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments, without requiring the execution of…
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Ziyang Tang, Yihao Feng, Lihong Li +2
Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al…
A Kernel Loss for Solving the Bellman Equation
Yihao Feng, Lihong Li, Qiang Liu
Value function learning plays a central role in many state-of-the-art reinforcement-learning algorithms. Many popular algorithms like Q-learning do not optimize any objective funct…