no code implementations • 2 Jan 2021 • Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao
The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances.
1 code implementation • 15 Oct 2020 • Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao
We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets.
Model-based Reinforcement Learning
Model Predictive Control
+4
no code implementations • 16 Sep 2020 • Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance.
no code implementations • 30 Jul 2020 • Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao
Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.
1 code implementation • NeurIPS 2020 • Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.
1 code implementation • 11 May 2020 • Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao
We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness.
1 code implementation • 11 May 2020 • Baiming Chen, Mengdi Xu, Liang Li, Ding Zhao
Action delays degrade the performance of reinforcement learning in many real-world systems.
no code implementations • 14 Apr 2020 • Baiming Chen, Xiang Chen, Wu Qiong, Liang Li
Results show that the adversarial scenarios generated by our method significantly degrade the performance of the tested vehicles.
no code implementations • 2 Mar 2020 • Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao
We then train the generative model as an agent (or a generator) to investigate the risky distribution parameters for a given driving algorithm being evaluated.