17 citations · 37 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
Wenhao Ding, Baiming Chen, Bo Li +2
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importan…
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 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…