no code implementations • 3 Dec 2022 • Yangang Ren, Yao Lyu, Wenxuan Wang, Shengbo Eben Li, Zeyang Li, Jingliang Duan
In this paper, we propose the smoothing policy iteration (SPI) algorithm to solve the zero-sum MGs approximately, where the maximum operator is replaced by the weighted LogSumExp (WLSE) function to obtain the nearly optimal equilibrium policies.
no code implementations • 19 Oct 2022 • Yang Guan, Liye Tang, Chuanxiao Li, Shengbo Eben Li, Yangang Ren, Junqing Wei, Bo Zhang, Keqiang Li
Self-evolution is indispensable to realize full autonomous driving.
no code implementations • 8 Oct 2022 • Zeyu Gao, Yao Mu, Ruoyan Shen, Chen Chen, Yangang Ren, Jianyu Chen, Shengbo Eben Li, Ping Luo, YanFeng Lu
End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals.
no code implementations • 24 Oct 2021 • Yangang Ren, Jianhua Jiang, Dongjie Yu, Shengbo Eben Li, Jingliang Duan, Chen Chen, Keqiang Li
This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows.
no code implementations • 30 Aug 2021 • Jianhua Jiang, Yangang Ren, Yang Guan, Shengbo Eben Li, Yuming Yin, Xiaoping Jin
Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and pedestrians.
2 code implementations • 18 Mar 2021 • Yang Guan, Yangang Ren, Qi Sun, Shengbo Eben Li, Haitong Ma, Jingliang Duan, Yifan Dai, Bo Cheng
In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.
1 code implementation • 2 Mar 2021 • Haitong Ma, Jianyu Chen, Shengbo Eben Li, Ziyu Lin, Yang Guan, Yangang Ren, Sifa Zheng
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.
no code implementations • 13 Feb 2020 • Yangang Ren, Jingliang Duan, Shengbo Eben Li, Yang Guan, Qi Sun
In this paper, we introduce the minimax formulation and distributional framework to improve the generalization ability of RL algorithms and develop the Minimax Distributional Soft Actor-Critic (Minimax DSAC) algorithm.
3 code implementations • 9 Jan 2020 • Jingliang Duan, Yang Guan, Shengbo Eben Li, Yangang Ren, Bo Cheng
In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance.
no code implementations • 23 Dec 2019 • Yang Guan, Shengbo Eben Li, Jingliang Duan, Jie Li, Yangang Ren, Qi Sun, Bo Cheng
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.