Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy
both cumulative and state-wise costs from offline datasets. This arrangement provides an …

Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-
critical real-world applications, such as autonomous driving, human-robot interaction, robot …

Satisfying safety constraints is a priority concern when solving optimal control problems
(OCPs). Due to the existence of infeasibility phenomenon, where a constraint-satisfying …

Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which
must be conducted in critical proximity to vital structures with a limited view of the anatomy …

Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety
guarantees hampers its real-world applicability. In particular, explorations during learning …

Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative
tasks, demonstrating impressive performance and scalability. However, deploying MARL …

Deep reinforcement learning (DRL) has demonstrated remarkable performance in many
continuous control tasks. However, a significant obstacle to the real-world application of …

In this paper, we seek to learn a robot policy guaranteed to satisfy state constraints. To
encourage constraint satisfaction, existing RL algorithms typically rely on Constrained …

In this paper, we develop a method for learning a control policy guaranteed to satisfy an
affine state constraint of high relative degree in closed loop with a black-box system …