18 citations · 75 across the 10 of their papers we have counts for
10 papers
Prompting Decision Transformer for Few-Shot Policy Generalization
Mengdi Xu, Yikang Shen, Shun Zhang +4
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve quick adaptat…
MHMS: Multimodal Hierarchical Multimedia Summarization
Jielin Qiu, Jiacheng Zhu, Mengdi Xu +6
Multimedia summarization with multimodal output can play an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or p…
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training
Peide Huang, Mengdi Xu, Fei Fang +1
Robust Reinforcement Learning (RL) focuses on improving performances under model errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust Adve…
Functional optimal transport: map estimation and domain adaptation for functional data
Jiacheng Zhu, Aritra Guha, Dat Do +3
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in te…
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen, Zuxin Liu, Jiacheng Zhu +3
Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the…
Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition
Wentai Lei, Mengdi Xu, Feifei Hou +1
In many scenarios where cameras are applied, such as robot positioning and unmanned driving, camera calibration is one of the most important pre-work. The interactive calibration m…
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Mengdi Xu, Wenhao Ding, Jiacheng Zhu +3
Continuously learning to solve unseen tasks with limited experience has been extensively pursued in meta-learning and continual learning, but with restricted assumptions such as ac…
Delay-Aware Model-Based Reinforcement Learning for Continuous Control
Baiming Chen, Mengdi Xu, Liang Li +1
Action delays degrade the performance of reinforcement learning in many real-world systems. This paper proposes a formal definition of delay-aware Markov Decision Process and prove…
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments
Baiming Chen, Mengdi Xu, Zuxin Liu +2
Action and observation delays exist prevalently in the real-world cyber-physical systems which may pose challenges in reinforcement learning design. It is particularly an arduous t…
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
Wenhao Ding, Mengdi Xu, Ding Zhao
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplicat…