174 citations · 356 across the 27 of their papers we have counts for
10 papers · 1 filter
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Baiyu Peng, Yao Mu, Yang Guan +3
Safety is essential for reinforcement learning (RL) applied in real-world situations. Chance constraints are suitable to represent the safety requirements in stochastic systems. Pr…
Numerically Stable Dynamic Bicycle Model for Discrete-time Control
Qiang Ge, Shengbo Eben Li, Qi Sun +1
Dynamic/kinematic model is of great significance in decision and control of intelligent vehicles. However, due to the singularity of dynamic models at low speed, kinematic models h…
Centralized Coordination of Connected Vehicles at Intersections using Graphical Mixed Integer Optimization
Qiang Ge, Qi Sun, Zhen Wang +3
This paper proposes a centralized multi-vehicle coordination scheme serving unsignalized intersections. The whole process consists of three stages: a) target velocity optimization:…
Reinforcement Solver for H-infinity Filter with Bounded Noise
Jie Li, Shengbo Eben Li, Kaiming Tang +2
H-infinity filter has been widely applied in engineering field, but copping with bounded noise is still an open problem and difficult to solve. This paper considers the H-infinity…
Ternary Policy Iteration Algorithm for Nonlinear Robust Control
Jie Li, Shengbo Eben Li, Yang Guan +3
The uncertainties in plant dynamics remain a challenge for nonlinear control problems. This paper develops a ternary policy iteration (TPI) algorithm for solving nonlinear robust c…
Continuous-time finite-horizon ADP for automated vehicle controller design with high efficiency
Ziyu Lin, Jingliang Duan, Shengbo Eben Li +2
The design of an automated vehicle controller can be generally formulated into an optimal control problem. This paper proposes a continuous-time finite-horizon approximate dynamicp…
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Lu Wen, Jingliang Duan, Shengbo Eben Li +2
Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to its potential to solve complex classification and control problems. However, existing RL…
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
Yao Mu, Shengbo Eben Li, Chang Liu +4
Reinforcement learning (RL) methods often rely on massive exploration data to search optimal policies, and suffer from poor sampling efficiency. This paper presents a mixed reinfor…
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
Yangang Ren, Jingliang Duan, Shengbo Eben Li +2
Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal p…
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to…