2 citations · 3 across the 17 of their papers we have counts for
5 papers · 1 filter
Off-Policy Reinforcement Learning with Anytime Safety Guarantees via Robust Safe Gradient Flow
Pol Mestres, Arnau Marzabal, Jorge Cortés
This paper considers the problem of solving constrained reinforcement learning (RL) problems with anytime guarantees, meaning that the algorithmic solution must yield a constraint-…
Universal Formulas for Safe Control and Their Neural Network Approximations
Pol Mestres, Jorge Cortés, Eduardo D. Sontag
We study the problem of designing a controller that satisfies an arbitrary number of affine inequalities at every point in the state space. This is motivated by the fact that a var…
Anytime Safe Reinforcement Learning
Pol Mestres, Arnau Marzabal, Jorge Cortés
This paper considers the problem of solving constrained reinforcement learning problems with anytime guarantees, meaning that the algorithmic solution returns a safe policy regardl…
Feedback Optimization with State Constraints through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that ari…
Control Barrier Function-Based Safety Filters: Characterization of Undesired Equilibria, Unbounded Trajectories, and Limit Cycles
Pol Mestres, Yiting Chen, Emiliano Dall'anese +1
This paper focuses on safety filters designed based on Control Barrier Functions (CBFs): these are modifications of a nominal stabilizing controller typically utilized in safety-cr…