73 citations · 77 across the 6 of their papers we have counts for
9 papers
Nuclear Norm Maximization Based Curiosity-Driven Learning
Chao Chen, Zijian Gao, Kele Xu +5
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in…
A Fixed Version of Quadratic Program in Gradient Episodic Memory
Wei Zhou, Yiying Li
Gradient Episodic Memory is indeed a novel method for continual learning, which solves new problems quickly without forgetting previously acquired knowledge. However, in the proces…
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning
Zijian Gao, Kele Xu, Bo Ding +3
Recently, deep reinforcement learning (RL) algorithms have made great progress in multi-agent domain. However, due to characteristics of RL, training for complex tasks would be res…
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
Zijian Gao, Kele Xu, Bo Ding +3
Recently, deep Reinforcement Learning (RL) algorithms have achieved dramatically progress in the multi-agent area. However, training the increasingly complex tasks would be time-co…
FedH2L: Federated Learning with Model and Statistical Heterogeneity
Yiying Li, Wei Zhou, Huaimin Wang +2
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. Mainstream FL approaches req…
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Wei Zhou, Yiying Li, Yongxin Yang +2
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic's action-value function is updated using temporal-di…