no code implementations • 6 Jun 2019 • Long Xin, Pin Wang, Ching-Yao Chan, Jianyu Chen, Shengbo Eben Li, Bo Cheng
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.
no code implementations • 11 Sep 2019 • Jingliang Duan, Jie Li, Qiang Ge, Shengbo Eben Li, Monimoy Bujarbaruah, Fei Ma, Dezhao Zhang
The warm-up phase minimizes the square of the Hamiltonian to achieve admissibility, while the generalized policy iteration phase relaxes the update termination conditions for faster convergence.
no code implementations • 26 Nov 2019 • Jingliang Duan, Zhengyu Liu, Shengbo Eben Li, Qi Sun, Zhenzhong Jia, Bo Cheng
CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem.
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.
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 • 28 Feb 2020 • Yao Mu, Shengbo Eben Li, Chang Liu, Qi Sun, Bingbing Nie, Bo Cheng, Baiyu Peng
This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.
no code implementations • 3 Mar 2020 • Lu Wen, Jingliang Duan, Shengbo Eben Li, Shaobing Xu, Huei Peng
The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
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 • 1 Jan 2021 • Yao Mu, Yuzheng Zhuang, Bin Wang, Wulong Liu, Shengbo Eben Li, Jianye Hao
The latent dynamics model summarizes an agent’s high dimensional experiences in a compact way.
no code implementations • 3 Mar 2017 • Y ang Zheng, Shengbo Eben Li, Keqiang Li, Francesco Borrelli
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a p r i o r i unknown desired set point.
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 • 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 • 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 • 23 Feb 2021 • Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Qi Sun, Bo Cheng
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems.
no code implementations • 20 Feb 2021 • Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Bo Cheng
This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), to solve large-scale nonlinear finite-horizon optimal control problems.
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).
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 • 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.
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 • 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 • 29 Jan 2022 • YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng
The recent advanced evolution-based zeroth-order optimization methods and the policy gradient-based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.
no code implementations • 6 Apr 2022 • Wenhan Cao, Jingliang Duan, Shengbo Eben Li, Chen Chen, Chang Liu, Yu Wang
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
no code implementations • 11 Sep 2022 • YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods.
no code implementations • 5 Oct 2022 • Wenhan Cao, Chang Liu, Zhiqian Lan, Shengbo Eben Li, Wei Pan, Angelo Alessandri
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers.
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 • 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 • 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 • 18 Apr 2023 • Yujie Yang, Zhilong Zheng, Shengbo Eben Li, Jingliang Duan, Jingjing Liu, Xianyuan Zhan, Ya-Qin Zhang
To address this challenge, we propose an indirect safe RL framework called feasible policy iteration, which guarantees that the feasible region monotonically expands and converges to the maximum one, and the state-value function monotonically improves and converges to the optimal one.
no code implementations • 13 Sep 2023 • Zeyang Li, Chuxiong Hu, Yunan Wang, Yujie Yang, Shengbo Eben Li
To address this issue, we propose a systematic framework to unify safe RL and robust RL, including problem formulation, iteration scheme, convergence analysis and practical algorithm design.
no code implementations • 18 Sep 2023 • Jie Li, Jiawei Wang, Shengbo Eben Li, Keqiang Li
Connected and automated vehicles (CAVs) technologies promise to attenuate undesired traffic disturbances.
no code implementations • 26 Sep 2023 • Zhiqian Lan, YuXuan Jiang, Yao Mu, Chen Chen, Shengbo Eben Li
Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments.
no code implementations • 4 Oct 2023 • Hao Sha, Yao Mu, YuXuan Jiang, Li Chen, Chenfeng Xu, Ping Luo, Shengbo Eben Li, Masayoshi Tomizuka, Wei Zhan, Mingyu Ding
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability.
no code implementations • 11 Oct 2023 • Zeyang Li, Chuxiong Hu, Yunan Wang, Guojian Zhan, Jie Li, Shengbo Eben Li
We also show that a modified version of regularized policy iteration, i. e., with finite-step policy evaluation, is equivalent to inexact Newton method where the Newton iteration formula is solved with truncated iterations.
no code implementations • 11 Oct 2023 • Zeyang Li, Chuxiong Hu, Shengbo Eben Li, Jia Cheng, Yunan Wang
To address this challenge, this paper proposes a robust safe reinforcement learning framework that tackles worst-case disturbances.
no code implementations • 29 Oct 2023 • Jingliang Duan, Jie Li, Xuyang Chen, Kai Zhao, Shengbo Eben Li, Lin Zhao
Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method.
no code implementations • 6 Nov 2023 • Xujie Song, Tong Liu, Shengbo Eben Li, Jingliang Duan, Wenxuan Wang, Keqiang Li
This paper proposes an Ising learning algorithm to train quantized neural network (QNN), by incorporating two essential techinques, namely binary representation of topological network and order reduction of loss function.
no code implementations • 4 Dec 2023 • Haoqi Yan, Haoyuan Xu, Hongbo Gao, Fei Ma, Shengbo Eben Li, Jingliang Duan
To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL).
no code implementations • 30 Jan 2024 • Yujie Yang, Zhilong Zheng, Shengbo Eben Li
This information restricts the time derivative of any unknown state to the intersection of a set of closed balls.
no code implementations • 4 Mar 2024 • Guojian Zhan, Ziang Zheng, Shengbo Eben Li
This paper for the first time introduces the concept of canonical data form for the purpose of achieving more effective design of datatic controllers.
no code implementations • 30 Mar 2024 • Wenhan Cao, Shiqi Liu, Chang Liu, Zeyu He, Stephen S. -T. Yau, Shengbo Eben Li
In this paper, we find that by adding an additional event that stipulates an inequality condition, we can transform the conditional probability into a special integration that is analogous to convolution.
no code implementations • 15 Apr 2024 • Yujie Yang, Zhilong Zheng, Shengbo Eben Li, Masayoshi Tomizuka, Changliu Liu
We demonstrate our feasibility theory by visualizing different feasible regions under both MPC and RL policies in an emergency braking control task.
1 code implementation • 19 Mar 2024 • Wenjun Zou, Yao Lyu, Jie Li, Yujie Yang, Shengbo Eben Li, Jingliang Duan, Xianyuan Zhan, Jingjing Liu, Yaqin Zhang, Keqiang Li
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems.
1 code implementation • 15 Nov 2021 • Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
1 code implementation • 25 Nov 2021 • Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Wenchao Sun, Jianyu Chen
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
1 code implementation • 14 Oct 2022 • Dongjie Yu, Wenjun Zou, Yujie Yang, Haitong Ma, Shengbo Eben Li, Jingliang Duan, Jianyu Chen
Furthermore, we build a safe RL framework to resolve constraints required by the DRC and its corresponding shield policy.
Model-based Reinforcement Learning reinforcement-learning +2
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.
1 code implementation • 29 Feb 2024 • Haicheng Liao, Yongkang Li, Zhenning Li, Chengyue Wang, Zhiyong Cui, Shengbo Eben Li, Chengzhong Xu
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency.
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 • 16 May 2022 • Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen
Recent studies incorporate feasible sets into CRL with energy-based methods such as control barrier function (CBF), safety index (SI), and leverage prior conservative estimations of feasible sets, which harms the performance of the learned policy.
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 • 19 Jan 2024 • Yinan Zheng, Jianxiong Li, Dongjie Yu, Yujie Yang, Shengbo Eben Li, Xianyuan Zhan, Jingjing Liu
Interestingly, we discover that via reachability analysis of safe-control theory, the hard safety constraint can be equivalently translated to identifying the largest feasible region given the offline dataset.
1 code implementation • 11 Dec 2023 • Haicheng Liao, Zhenning Li, Huanming Shen, Wenxuan Zeng, Dongping Liao, Guofa Li, Shengbo Eben Li, Chengzhong Xu
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles.
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.
1 code implementation • 9 Oct 2023 • Jingliang Duan, Wenxuan Wang, Liming Xiao, Jiaxin Gao, Shengbo Eben Li
Reinforcement learning (RL) has proven to be highly effective in tackling complex decision-making and control tasks.
4 code implementations • 23 Jan 2020 • Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka
A sequential latent environment model is introduced and learned jointly with the reinforcement learning process.