44 citations · 48 across the 7 of their papers we have counts for
8 papers
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
Jacob Beck, Risto Vuorio, Zheng Xiong +1
Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency. Meta-RL directly addresses this sample inefficiency by learning to perform few-sho…
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System
Zheng Xiong, Biao Luo, Bing-Chuan Wang +3
This paper develops a Decentralized Multi-Agent Reinforcement Learning (Dec-MARL) method to solve the SoC balancing problem in the distributed energy storage system (DESS). First,…
Single-View View Synthesis with Self-Rectified Pseudo-Stereo
Yang Zhou, Hanjie Wu, Wenxi Liu +3
Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view vie…
Universal Morphology Control via Contextual Modulation
Zheng Xiong, Jacob Beck, Shimon Whiteson
Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. However, it poses a challeng…
A Tutorial on Meta-Reinforcement Learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu +4
While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficien…
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting
Zheng Xiong, Liangyu Chai, Wenxi Liu +3
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-…