29 citations · 54 across the 14 of their papers we have counts for
14 papers
Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning
Hao Chen, Chen Gong, Yizhe Wang +1
A backdoor attack allows a malicious user to manipulate the environment or corrupt the training data, thus inserting a backdoor into the trained agent. Such attacks compromise the…
Centralized Cooperative Exploration Policy for Continuous Control Tasks
Chao Li, Chen Gong, Qiang He +2
The deep reinforcement learning (DRL) algorithm works brilliantly on solving various complex control tasks. This phenomenal success can be partly attributed to DRL encouraging inte…
GAN Inversion for Image Editing via Unsupervised Domain Adaptation
Siyu Xing, Chen Gong, Hewei Guo +3
Existing GAN inversion methods work brilliantly in reconstructing high-quality (HQ) images while struggling with more common low-quality (LQ) inputs in practical application. To ad…
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Chen Gong, Zhou Yang, Yunpeng Bai +8
Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL…
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
Qiang He, Huangyuan Su, Jieyu Zhang +1
Deep reinforcement learning (DRL) gives the promise that an agent learns good policy from high-dimensional information, whereas representation learning removes irrelevant and redun…
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
Yunpeng Bai, Chen Gong, Bin Zhang +3
Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an imp…
The -Divergence Reinforcement Learning Framework
Chen Gong, Qiang He, Yunpeng Bai +6
The framework of deep reinforcement learning (DRL) provides a powerful and widely applicable mathematical formalization for sequential decision-making. This paper present a novel D…
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders
Xiaoyu Chen, Chen Gong, Qiang He +2
Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications. However, it is alw…
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning
Qiang He, Huangyuan Su, Chen Gong +1
During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time. Therefore, there is a ris…
POPO: Pessimistic Offline Policy Optimization
Qiang He, Xinwen Hou
Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment. This setting offers…
WD3: Taming the Estimation Bias in Deep Reinforcement Learning
Qiang He, Xinwen Hou
The overestimation phenomenon caused by function approximation is a well-known issue in value-based reinforcement learning algorithms such as deep Q-networks and DDPG, which could…
Highway Transformer: Self-Gating Enhanced Self-Attentive Networks
Yekun Chai, Shuo Jin, Xinwen Hou
Self-attention mechanisms have made striking state-of-the-art (SOTA) progress in various sequence learning tasks, standing on the multi-headed dot product attention by attending to…
Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning
Runsheng Yu, Zhenyu Shi, Xinrun Wang +5
Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized t…
Learning Representations in Reinforcement Learning:An Information Bottleneck Approach
Pei Yingjun, Hou Xinwen
The information bottleneck principle is an elegant and useful approach to representation learning. In this paper, we investigate the problem of representation learning in the conte…