20 citations · 38 across the 9 of their papers we have counts for
7 papers · 1 filter
Robust Imitation Learning from Corrupted Demonstrations
Liu Liu, Ziyang Tang, Lanqing Li +1
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers. Classical approaches such as Behavio…
MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
Wanpeng Zhang, Xi Xiao, Yao Yao +2
Model-based reinforcement learning is a widely accepted solution for solving excessive sample demands. However, the predictions of the dynamics models are often not accurate enough…
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
Yao Yao, Li Xiao, Zhicheng An +2
Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics. However, there are some remaining is…
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Rui Yang, Jiafei Lyu, Yu Yang +5
Multi-goal reinforcement learning is widely applied in planning and robot manipulation. Two main challenges in multi-goal reinforcement learning are sparse rewards and sample ineff…
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Lanqing Li, Yuanhao Huang, Mingzhe Chen +3
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications. A pop…
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
Lanqing Li, Rui Yang, Dijun Luo
We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any intera…
Label-Aware Graph Convolutional Networks
Hao Chen, Yue Xu, Feiran Huang +5
Recent advances in Graph Convolutional Networks (GCNs) have led to state-of-the-art performance on various graph-related tasks. However, most existing GCN models do not explicitly…