29 citations · 54 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…