4 papers
SB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints
Dominik Wagner, Ankit Kanwar, Luke Ong
In safety-critical domains, reinforcement learning (RL) agents must often satisfy strict, zero-cost safety constraints while accomplishing tasks. Existing model-free methods freque…
PAC One-Step Safety Certification for Black-Box Discrete-Time Stochastic Systems
Taoran Wu, Dominik Wagner, Jingduo Pan +3
This paper investigates the problem of safety certification for black-box discrete-time stochastic systems, where both the system dynamics and disturbance distributions are unknown…
Comparative Analysis of Barrier-like Function Methods for Reach-Avoid Verification in Stochastic Discrete-Time Systems
Zhipeng Cao, Peixin Wang, Luke Ong +3
In this paper, we compare several representative barrier-like conditions from the literature for infinite-horizon reach-avoid verification of stochastic discrete-time systems. Our…
Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives
Dominik Wagner, Leon Witzman, Luke Ong
Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $…