6 papers
Augmented Lagrangian Multiplier Network for State-wise Safety in Reinforcement Learning
Jiaming Zhang, Yujie Yang, Yao Lyu +2
Safety is a primary challenge in real-world reinforcement learning (RL). Formulating safety requirements as state-wise constraints has become a prominent paradigm. Handling state-w…
On the Equilibrium between Feasible Zone and Uncertain Model in Safe Exploration
Yujie Yang, Zhilong Zheng, Shengbo Eben Li
Ensuring the safety of environmental exploration is a critical problem in reinforcement learning (RL). While limiting exploration to a feasible zone has become widely accepted as a…
The Feasibility Theory of Constrained Reinforcement Learning: A Tutorial Study
Yujie Yang, Zhilong Zheng, Masayoshi Tomizuka +2
Satisfying safety constraints is a priority concern when solving optimal control problems (OCPs). Due to the existence of infeasibility phenomenon, where a constraint-satisfying so…
Exchange Policy Optimization Algorithm for Semi-Infinite Safe Reinforcement Learning
Jiaming Zhang, Yujie Yang, Haoning Wang +2
Safe reinforcement learning (safe RL) aims to respect safety requirements while optimizing long-term performance. In many practical applications, however, the problem involves an i…
Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks
Jiaxing Li, Hanjiang Hu, Yujie Yang +1
Recent learning-based safety filters have outperformed conventional methods, such as hand-crafted Control Barrier Functions (CBFs), by effectively adapting to complex constraints.…
Feasible Policy Iteration for Safe Reinforcement Learning
Yujie Yang, Zhilong Zheng, Shengbo Eben Li +4
Safety is the priority concern when applying reinforcement learning (RL) algorithms to real-world control problems. While policy iteration provides a fundamental algorithm for stan…