13 citations · 14 across the 4 of their papers we have counts for
4 papers
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Mingyang Wang, Zhenshan Bing, Xiangtong Yao +5
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing…
Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor
Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen +4
Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the signific…
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Zheng Yu, Yikuan Li, Joseph Kim +3
Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test pane…
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions
Xiangtong Yao, Zhenshan Bing, Genghang Zhuang +4
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-…