activity
20182021
most citedSequential Evaluation and Generation Framework for Combinatorial Recommender System

8 citations · 23 across the 8 of their papers we have counts for

collaborators

10 papers

cs.RO20211 cited

Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

Haojie Shi, Bo Zhou, Hongsheng Zeng +6

Recently reinforcement learning (RL) has emerged as a promising approach for quadrupedal locomotion, which can save the manual effort in conventional approaches such as designing s…

cs.LG20211 cited

ADER:Adapting between Exploration and Robustness for Actor-Critic Methods

Bo Zhou, Kejiao Li, Hongsheng Zeng +2

Combining off-policy reinforcement learning methods with function approximators such as neural networks has been found to lead to overestimation of the value function and sub-optim…

cs.AI20213 cited

Action Set Based Policy Optimization for Safe Power Grid Management

Bo Zhou, Hongsheng Zeng, Yuecheng Liu +3

Maintaining the stability of the modern power grid is becoming increasingly difficult due to fluctuating power consumption, unstable power supply coming from renewable energies, an…

cs.RO2020

Proactive Interaction Framework for Intelligent Social Receptionist Robots

Yang Xue, Fan Wang, Hao Tian +4

Proactive human-robot interaction (HRI) allows the receptionist robots to actively greet people and offer services based on vision, which has been found to improve acceptability an…

cs.LG20196 cited

Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion

Bo Zhou, Hongsheng Zeng, Fan Wang +2

By integrating dynamics models into model-free reinforcement learning (RL) methods, model-based value expansion (MVE) algorithms have shown a significant advantage in sample effici…

cs.IR20194 cited

Learning to Recommend via Meta Parameter Partition

Liang Zhao, Yang Wang, Daxiang Dong +1

In this paper we propose to solve an important problem in recommendation -- user cold start, based on meta leaning method. Previous meta learning approaches finetune all parameters…