Shengbo Eben Li

research

∙ 12/03/2022

Smoothing Policy Iteration for Zero-sum Markov Games

Zero-sum Markov Games (MGs) has been an efficient framework for multi-ag...

0 Yangang Ren, et al. ∙

research

∙ 10/19/2022

Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification

Self-evolution is indispensable to realize full autonomous driving. This...

0 Yang Guan, et al. ∙

research

∙ 10/14/2022

Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate

Safe reinforcement learning (RL) that solves constraint-satisfactory pol...

0 Dongjie Yu, et al. ∙

research

∙ 09/23/2022

Synthesize Efficient Safety Certificates for Learning-Based Safe Control using Magnitude Regularization

Energy-function-based safety certificates can provide provable safety gu...

0 Haotian Zheng, et al. ∙

research

∙ 09/11/2022

Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning

Choosing an appropriate parameter set for the designed controller is cri...

0 Yuheng Lei, et al. ∙

research

∙ 05/16/2022

Reachability Constrained Reinforcement Learning

Constrained reinforcement learning (CRL) has gained significant interest...

0 Dongjie Yu, et al. ∙

research

∙ 04/09/2022

Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning

Intersections are quite challenging among various driving scenes wherein...

0 Yangang Ren, et al. ∙

research

∙ 01/29/2022

Zeroth-Order Actor-Critic

Zeroth-order optimization methods and policy gradient based first-order ...

0 Yuheng Lei, et al. ∙

research

∙ 11/25/2021

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

In the trial-and-error mechanism of reinforcement learning (RL), a notor...

0 Haitong Ma, et al. ∙

research

∙ 11/15/2021

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

Safety is the major consideration in controlling complex dynamical syste...

0 Haitong Ma, et al. ∙

research

∙ 10/24/2021

Encoding Integrated Decision and Control for Autonomous Driving with Mixed Traffic Flow

Reinforcement learning (RL) has been widely adopted to make intelligent ...

0 Yangang Ren, et al. ∙

research

∙ 09/12/2021

Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios

In this paper, we propose a new reinforcement learning (RL) algorithm, c...

0 Jingliang Duan, et al. ∙

research

∙ 08/30/2021

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

Autonomous driving at intersections is one of the most complicated and a...

0 Jianhua Jiang, et al. ∙

research

∙ 08/26/2021

Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian

Safety is essential for reinforcement learning (RL) applied in the real ...

0 Baiyu Peng, et al. ∙

research

∙ 05/24/2021

Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving

In this paper, we propose a new state representation method, called enco...

0 Jingliang Duan, et al. ∙

research

∙ 03/18/2021

Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence

Decision and control are two of the core functionalities of high-level a...

3 Yang Guan, et al. ∙

research

∙ 03/09/2021

Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

State estimation is critical to control systems, especially when the sta...

0 Kaiming Tang, et al. ∙

research

∙ 03/08/2021

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

Merging into the highway from the on-ramp is an essential scenario for a...

0 Yiting Kong, et al. ∙

research

∙ 03/02/2021

Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

Model information can be used to predict future trajectories, so it has ...

0 Haitong Ma, et al. ∙

research

∙ 02/23/2021

Recurrent Model Predictive Control

This paper proposes an off-line algorithm, called Recurrent Model Predic...

0 Zhengyu Liu, et al. ∙

research

∙ 02/23/2021

Mixed Policy Gradient

Reinforcement learning (RL) has great potential in sequential decision-m...

13 Yang Guan, et al. ∙

research

∙ 02/17/2021

Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

Safety is essential for reinforcement learning (RL) applied in real-worl...

0 Baiyu Peng, et al. ∙

research

∙ 02/16/2021

Steadily Learn to Drive with Virtual Memory

Reinforcement learning has shown great potential in developing high-leve...

0 Yuhang Zhang, et al. ∙

research

∙ 12/19/2020

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

Safety constraints are essential for reinforcement learning (RL) applied...

0 Baiyu Peng, et al. ∙

research

∙ 07/14/2020

Ternary Policy Iteration Algorithm for Nonlinear Robust Control

The uncertainties in plant dynamics remain a challenge for nonlinear con...

0 Jie Li, et al. ∙

research

∙ 03/03/2020

Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Reinforcement learning (RL) is attracting increasing interests in autono...

0 Lu Wen, et al. ∙

research

∙ 02/28/2020

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Reinforcement learning (RL) methods often rely on massive exploration da...

30 Yao Mu, et al. ∙

research

∙ 02/13/2020

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

Reinforcement learning (RL) has achieved remarkable performance in a var...

0 Yangang Ren, et al. ∙

research

∙ 01/23/2020

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

Unlike popular modularized framework, end-to-end autonomous driving seek...

38 Jianyu Chen, et al. ∙

research

∙ 01/09/2020

Addressing Value Estimation Errors in Reinforcement Learning with a State-Action Return Distribution Function

In current reinforcement learning (RL) methods, function approximation e...

0 Jingliang Duan, et al. ∙

research

∙ 12/23/2019

Direct and indirect reinforcement learning

Reinforcement learning (RL) algorithms have been successfully applied to...

0 Yang Guan, et al. ∙

research

∙ 12/18/2019

Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization

Connected vehicles will change the modes of future transportation manage...

0 Yang Guan, et al. ∙

research

∙ 11/26/2019

Deep adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints

This paper presents a constrained deep adaptive dynamic programming (CDA...

0 Jingliang Duan, et al. ∙

research

∙ 09/11/2019

Generalized Policy Iteration for Optimal Control in Continuous Time

This paper proposes the Deep Generalized Policy Iteration (DGPI) algorit...

0 Jingliang Duan, et al. ∙

research

∙ 06/06/2019

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

As autonomous vehicles (AVs) need to interact with other road users, it ...

0 Long Xin, et al. ∙

Shengbo Eben Li

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