8 citations · 11 across the 8 of their papers we have counts for
10 papers
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
Kaiming Tang, Shengbo Eben Li, Yuming Yin +4
State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use pol…
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
Yiting Kong, Yang Guan, Jingliang Duan +3
Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performa…
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Haitong Ma, Jianyu Chen, Shengbo Eben Li +4
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,…
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Baiyu Peng, Yao Mu, Jingliang Duan +3
Safety is essential for reinforcement learning (RL) applied in real-world tasks like autonomous driving. Chance constraints which guarantee the satisfaction of state constraints at…
Steadily Learn to Drive with Virtual Memory
Yuhang Zhang, Yao Mu, Yujie Yang +4
Reinforcement learning has shown great potential in developing high-level autonomous driving. However, for high-dimensional tasks, current RL methods suffer from low data efficienc…
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…