Search Results for author: Baiming Chen

Found 9 papers, 4 papers with code

Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method

1 code implementation15 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 reinforcement-learning +1

Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

no code implementations16 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.

Decision Making

MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

no code implementations30 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.

reinforcement-learning

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

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.

Continual Learning Decision Making +5

Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments

1 code implementation11 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.

Autonomous Vehicles Multi-agent Reinforcement Learning +1

Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios

no code implementations14 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.

Autonomous Driving

Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

no code implementations2 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.

Autonomous Driving

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