4 citations · 12 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
Exchange Policy Optimization Algorithm for Semi-Infinite Safe Reinforcement Learning
Jiaming Zhang, Yujie Yang, Haoning Wang +2
Safe reinforcement learning (RL) aims to optimize long-term performance while adhering to safety requirements. However, many practical applications involve an infinite number of co…
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.…
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Yuxuan Jiang, Yujie Yang, Zhiqian Lan +6
Rocket recycling is a crucial pursuit in aerospace technology, aimed at reducing costs and environmental impact in space exploration. The primary focus centers on rocket landing co…
Policy Bifurcation in Safe Reinforcement Learning
Wenjun Zou, Yao Lyu, Jie Li +7
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, wh…
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Yinan Zheng, Jianxiong Li, Dongjie Yu +4
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning. Most existing methods only enforce soft constraints, i.e., constraining safety…
Safe Reinforcement Learning with Dual Robustness
Zeyang Li, Chuxiong Hu, Yunan Wang +2
Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or compromise safety specifications. Existing methods either a…
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…
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…