Search Results for author: Yang Guan

Found 16 papers, 5 papers with code

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

Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

1 code implementation22 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.

reinforcement-learning Safe Exploration +1

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

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

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

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.

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

CTM: Collaborative Temporal Modeling for Action Recognition

no code implementations8 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.

Action Recognition Video Understanding

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

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