20 citations · 38 across the 9 of their papers we have counts for
6 papers · 1 filter
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control
Wanpeng Zhang, Xiaoyan Cao, Yao Yao +3
Due to the high efficiency and less weather dependency, autonomous greenhouses provide an ideal solution to meet the increasing demand for fresh food. However, managers are faced w…
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…
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
Xiaoyan Cao, Yao Yao, Lanqing Li +8
Agriculture is the foundation of human civilization. However, the rapid increase of the global population poses a challenge on this cornerstone by demanding more food. Modern auton…
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…