27 citations · 35 across the 6 of their papers we have counts for
6 papers
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Jiafei Lyu, Xiaoteng Ma, Le Wan +3
Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the enviro…
Cross-Domain Policy Adaptation via Value-Guided Data Filtering
Kang Xu, Chenjia Bai, Xiaoteng Ma +5
Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. For example, a robot learns the policy in a simulator…
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
Junjie Zhang, Jiafei Lyu, Xiaoteng Ma +4
Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, e…
Single-Trajectory Distributionally Robust Reinforcement Learning
Zhipeng Liang, Xiaoteng Ma, Jose Blanchet +2
To mitigate the limitation that the classical reinforcement learning (RL) framework heavily relies on identical training and test environments, Distributionally Robust RL (DRRL) ha…
Mildly Conservative Q-Learning for Offline Reinforcement Learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li +1
Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between t…
Efficient Continuous Control with Double Actors and Regularized Critics
Jiafei Lyu, Xiaoteng Ma, Jiangpeng Yan +1
How to obtain good value estimation is one of the key problems in Reinforcement Learning (RL). Current value estimation methods, such as DDPG and TD3, suffer from unnecessary over-…