Search Results for author: Shengbo Eben Li

Found 30 papers, 5 papers with code

Reachability Constrained Reinforcement Learning

no code implementations16 May 2022 Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen

We characterize the feasible set by the established self-consistency condition, then a safety value function can be learned and used as constraints in CRL.

reinforcement-learning

Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees

no code implementations6 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.

Zeroth-Order Actor-Critic

no code implementations29 Jan 2022 YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng

Zeroth-order optimization methods and policy gradient based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.

Continuous Control

Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning

no code implementations25 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.

reinforcement-learning

Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning

no code implementations15 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.

reinforcement-learning

Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections

no code implementations24 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.

Autonomous Driving Decision Making

Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow

no code implementations30 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.

Autonomous Driving

Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence

2 code implementations18 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.

Autonomous Driving Model-based Reinforcement Learning +1

Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

no code implementations9 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.

Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm

no code implementations8 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).

Decision Making

Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

1 code implementation2 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.

Autonomous Driving reinforcement-learning +1

Recurrent Model Predictive Control

no code implementations23 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.

Mixed Policy Gradient

2 code implementations23 Feb 2021 Yang Guan, Jingliang Duan, Shengbo Eben Li, Jie Li, Jianyu Chen, Bo Cheng

MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.

Decision Making

Recurrent Model Predictive Control: Learning an Explicit Recurrent Controller for Nonlinear Systems

no code implementations20 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.

Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

no code implementations17 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.

Autonomous Driving reinforcement-learning

Steadily Learn to Drive with Virtual Memory

no code implementations16 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.

Autonomous Driving

Robust Memory Augmentation by Constrained Latent Imagination

no code implementations1 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.

Model-Based Actor-Critic with Chance Constraint for Stochastic System

no code implementations19 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.

Ternary Policy Iteration Algorithm for Nonlinear Robust Control

no code implementations14 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.

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

no code implementations28 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.

reinforcement-learning

Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic

no code implementations13 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.

Autonomous Driving Decision Making +1

Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

2 code implementations9 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.

Continuous Control reinforcement-learning

Direct and indirect reinforcement learning

no code implementations23 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.

Decision Making reinforcement-learning

Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints

no code implementations26 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.

Generalized Policy Iteration for Optimal Control in Continuous Time

no code implementations11 Sep 2019 Jingliang Duan, Shengbo Eben Li, Zhengyu Liu, Monimoy Bujarbaruah, Bo Cheng

This paper proposes the Deep Generalized Policy Iteration (DGPI) algorithm to find the infinite horizon optimal control policy for general nonlinear continuous-time systems with known dynamics.

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

no code implementations6 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.

Autonomous Vehicles feature selection +2

Distributed Model Predictive Control for Heterogeneous V ehicle Platoons Under Unidirectional Topologies

no code implementations3 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.

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