166 citations · 222 across the 6 of their papers we have counts for
6 papers
Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Ziyi Liu, Grace Zhang
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.g., p…
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
Grace Zhang, Ayush Jain, Injune Hwang +2
Multi-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by…
CoMPS: Continual Meta Policy Search
Glen Berseth, Zhiwei Zhang, Grace Zhang +2
We develop a new continual meta-learning method to address challenges in sequential multi-task learning. In this setting, the agent's goal is to achieve high reward over any sequen…
Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding
Grace Zhang, Linghan Zhong, Youngwoon Lee +1
The ability to transfer a policy from one environment to another is a promising avenue for efficient robot learning in realistic settings where task supervision is not available. T…
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
Xue Bin Peng, Aviral Kumar, Grace Zhang +1
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that…
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
Xue Bin Peng, Michael Chang, Grace Zhang +2
Humans are able to perform a myriad of sophisticated tasks by drawing upon skills acquired through prior experience. For autonomous agents to have this capability, they must be abl…