Yang Guan | DeepAI

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Jingliang Duan
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∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 05/22/2021

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

The safety constraints commonly used by existing safe reinforcement lear...

0 Haitong Ma, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 02/23/2021

Mixed Policy Gradient

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

13 Yang Guan, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 02/08/2020

CTM: Collaborative Temporal Modeling for Action Recognition

With the rapid development of digital multimedia, video understanding ha...

1 Qian Liu, et al. ∙

∙ 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. ∙

∙ 12/23/2019

Direct and indirect reinforcement learning

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

0 Yang Guan, et al. ∙

∙ 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. ∙

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