5 citations · 9 across the 5 of their papers we have counts for
5 papers
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Ruijie Zheng, Xiyao Wang, Yanchao Sun +5
Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address t…
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
Ruijie Zheng, Xiyao Wang, Huazhe Xu +1
Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance…
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Xiyao Wang, Wichayaporn Wongkamjan, Furong Huang
Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning.…
Transfer RL across Observation Feature Spaces via Model-Based Regularization
Yanchao Sun, Ruijie Zheng, Xiyao Wang +2
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramati…
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
Xiyao Wang, Junge Zhang, Wenzhen Huang +1
Model-based reinforcement learning (MBRL) is believed to have higher sample efficiency compared with model-free reinforcement learning (MFRL). However, MBRL is plagued by dynamics…