17 citations · 89 across the 9 of their papers we have counts for
7 papers · 1 filter
Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
Zuxin Liu, Hongyi Zhou, Baiming Chen +3
This paper studies the constrained/safe reinforcement learning (RL) problem with sparse indicator signals for constraint violations. We propose a model-based approach to enable RL…
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…
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments
Zuxin Liu, Baiming Chen, Hongyi Zhou +3
Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications. This paper proposes a decentrali…
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…