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 • 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.
no code implementations • 26 Aug 2021 • Baiyu Peng, Jingliang Duan, Jianyu Chen, Shengbo Eben Li, Genjin Xie, Congsheng Zhang, Yang Guan, Yao Mu, Enxin Sun
Based on this, the penalty method is formulated as a proportional controller, and the Lagrangian method is formulated as an integral controller.
3 code implementations • 22 May 2021 • Haitong Ma, Yang Guan, Shegnbo Eben Li, Xiangteng Zhang, Sifa Zheng, Jianyu Chen
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.
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.
no code implementations • 9 Mar 2021 • Kaiming Tang, Shengbo Eben Li, Yuming Yin, Yang Guan, Jingliang Duan, Wenhan Cao, Jie Li
The equivalence holds given certain conditions about initial state distributions and policy formats, in which the system state is the estimation error, control input is the filter gain, and control objective function is the accumulated estimation error.
no code implementations • 8 Mar 2021 • Yiting Kong, Yang Guan, Jingliang Duan, Shengbo Eben Li, Qi Sun, Bingbing Nie
In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC).
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.
2 code implementations • 23 Feb 2021 • Yang Guan, Jingliang Duan, Shengbo Eben Li, Jie Li, Jianyu Chen, Bo Cheng
Formally, MPG is constructed as a weighted average of the data-driven and model-driven PGs, where the former is the derivative of the learned Q-value function, and the latter is that of the model-predictive return.
no code implementations • 17 Feb 2021 • Baiyu Peng, Yao Mu, Jingliang Duan, Yang Guan, Shengbo Eben Li, Jianyu Chen
Taking a control perspective, we first interpret the penalty method and the Lagrangian method as proportional feedback and integral feedback control, respectively.
no code implementations • 16 Feb 2021 • Yuhang Zhang, Yao Mu, Yujie Yang, Yang Guan, Shengbo Eben Li, Qi Sun, Jianyu Chen
Reinforcement learning has shown great potential in developing high-level autonomous driving.
no code implementations • 19 Dec 2020 • Baiyu Peng, Yao Mu, Yang Guan, Shengbo Eben Li, Yuming Yin, Jianyu Chen
Safety is essential for reinforcement learning (RL) applied in real-world situations.
no code implementations • 14 Jul 2020 • Jie Li, Shengbo Eben Li, Yang Guan, Jingliang Duan, Wenyu Li, Yuming Yin
The simulation results show that the TPI algorithm can converge to the optimal solution for the linear plant, and has high resistance to disturbances for the nonlinear plant.
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.
no code implementations • 8 Feb 2020 • Qian Liu, Tao Wang, Jie Liu, Yang Guan, Qi Bu, Longfei Yang
In order to learn powerful feature of videos, we propose a Collaborative Temporal Modeling (CTM) block (Figure 1) to learn temporal information for action recognition.
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.