37 citations · 138 across the 17 of their papers we have counts for
6 papers · 1 filter
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
Nicklas Hansen, Yixin Lin, Hao Su +3
Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-…
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Nicklas Hansen, Zhecheng Yuan, Yanjie Ze +5
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks. We revisit a simple Learning-from-Scratch (LfS) baseline that incorporates data augmentat…
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Yifan Xu, Nicklas Hansen, Zirui Wang +3
Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so…
Improving Policy Optimization with Generalist-Specialist Learning
Zhiwei Jia, Xuanlin Li, Zhan Ling +3
Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically ob…
Provably Efficient Kernelized Q-Learning
Shuang Liu, Hao Su
We propose and analyze a kernelized version of Q-learning. Although a kernel space is typically infinite-dimensional, extensive study has shown that generalization is only affected…
Temporal Difference Learning for Model Predictive Control
Nicklas Hansen, Xiaolong Wang, Hao Su
Data-driven model predictive control has two key advantages over model-free methods: a potential for improved sample efficiency through model learning, and better performance as co…