no code implementations • 2 May 2024 • Zhongchang Sun, Sihong He, Fei Miao, Shaofeng Zou
Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment.
no code implementations • 23 Aug 2023 • Jiangwei Wang, Lili Su, Songyang Han, Dongjin Song, Fei Miao
Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic.
no code implementations • 30 Jul 2023 • Sihong He, Shuo Han, Fei Miao
In this work, we design a multi-agent reinforcement learning (MARL)-based framework for EAVs balancing in E-AMoD systems, with adversarial agents to model both the EAVs supply and mobility demand uncertainties that may undermine the vehicle balancing solutions.
1 code implementation • 30 Jul 2023 • Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, Fei Miao
Then we propose a robust multi-agent Q-learning (RMAQ) algorithm to find such an equilibrium, with convergence guarantees.
no code implementations • 11 Jun 2023 • Jiangwei Wang, Shuo Yang, Ziyan An, Songyang Han, Zhili Zhang, Rahul Mangharam, Meiyi Ma, Fei Miao
The STL requirements are designed to include both task specifications according to the objective of each agent and safety specifications, and the robustness values of the STL specifications are leveraged to generate rewards.
no code implementations • 8 Apr 2023 • Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin, Deniz Gurevin, Fei Miao, Caiwen Ding
We evaluate our method on image classification tasks using CIFAR-10 and ImageNet with state-of-the-art MLP-Mixer, Swin Transformer, and VGG-16, ResNet-18, ResNet-50 and ResNet-110, MobileNetV2.
no code implementations • 25 Mar 2023 • Sanbao Su, Songyang Han, Yiming Li, Zhili Zhang, Chen Feng, Caiwen Ding, Fei Miao
MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation.
no code implementations • 8 Mar 2023 • Muzi Peng, Jiangwei Wang, Dongjin Song, Fei Miao, Lili Su
Deep learning is the method of choice for trajectory prediction for autonomous vehicles.
no code implementations • 8 Feb 2023 • Songyang Han, Shanglin Zhou, Lynn Pepin, Jiangwei Wang, Caiwen Ding, Fei Miao
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles.
1 code implementation • 6 Dec 2022 • Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 5 Oct 2022 • Zhili Zhang, Songyang Han, Jiangwei Wang, Fei Miao
With the experiment deployed in the CARLA simulator, we verify the performance of the safety checking, spatial-temporal encoder, and coordination mechanisms designed in our method by comparative experiments in several challenging scenarios with unconnected hazard vehicles.
no code implementations • 17 Sep 2022 • Sihong He, Yue Wang, Shuo Han, Shaofeng Zou, Fei Miao
In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with state transition kernel uncertainty for EV AMoD systems.
1 code implementation • 16 Sep 2022 • Sanbao Su, Yiming Li, Sihong He, Songyang Han, Chen Feng, Caiwen Ding, Fei Miao
Our work is the first to estimate the uncertainty of collaborative object detection.
no code implementations • 14 Sep 2022 • Yue Wang, Fei Miao, Shaofeng Zou
We then investigate a concrete example of $\delta$-contamination uncertainty set, design an online and model-free algorithm and theoretically characterize its sample complexity.
no code implementations • EMNLP 2021 • Jieren Deng, Chenghong Wang, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
no code implementations • 18 Dec 2020 • Deniz Gurevin, Shanglin Zhou, Lynn Pepin, Bingbing Li, Mikhail Bragin, Caiwen Ding, Fei Miao
We further accelerate the convergence of the SLR by using quadratic penalties.
no code implementations • 9 Mar 2020 • Songyang Han, Shanglin Zhou, Jiangwei Wang, Lynn Pepin, Caiwen Ding, Jie Fu, Fei Miao
The truncated Q-function utilizes the shared information from neighboring CAVs such that the joint state and action spaces of the Q-function do not grow in our algorithm for a large-scale CAV system.