8 citations · 37 across the 22 of their papers we have counts for
8 papers · 1 filter
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Baiyu Peng, Jingliang Duan, Jianyu Chen +6
Safety is essential for reinforcement learning (RL) applied in the real world. Adding chance constraints (or probabilistic constraints) is a suitable way to enhance RL safety under…
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
Yang Guan, Yangang Ren, Qi Sun +5
Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learnin…
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…
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…