20 citations · 35 across the 9 of their papers we have counts for
6 papers · 1 filter
Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
Wanpeng Zhang, Yilin Li, Boyu Yang +1
In real-world scenarios, the application of reinforcement learning is significantly challenged by complex non-stationarity. Most existing methods attempt to model changes in the en…
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
Ziluo Ding, Wanpeng Zhang, Junpeng Yue +3
We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also…
Model-Based Opponent Modeling
Xiaopeng Yu, Jiechuan Jiang, Wanpeng Zhang +2
When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before. Modeling the behaviors, goals, or beliefs of opponents coul…
MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
Wanpeng Zhang, Xi Xiao, Yao Yao +2
Model-based reinforcement learning is a widely accepted solution for solving excessive sample demands. However, the predictions of the dynamics models are often not accurate enough…
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
Yao Yao, Li Xiao, Zhicheng An +2
Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics. However, there are some remaining is…
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
Bowen Zhao, Xi Xiao, Wanpeng Zhang +2
Principal Component Analysis (PCA) is a popular tool for dimensionality reduction and feature extraction in data analysis. There is a probabilistic version of PCA, known as Probabi…