Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning

2 code implementations • 21 Jul 2022 • Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause

We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial.

reinforcement-learning Reinforcement Learning (RL) +2

Constrained Policy Optimization via Bayesian World Models

1 code implementation • ICLR 2022 • Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause

Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications.

reinforcement-learning Reinforcement Learning (RL)

Risk-averse Heteroscedastic Bayesian Optimization

1 code implementation • NeurIPS 2021 • Anastasiia Makarova, Ilnura Usmanova, Ilija Bogunovic, Andreas Krause

We generalize BO to trade mean and input-dependent variance of the objective, both of which we assume to be unknown a priori.

Bayesian Optimization

Safe non-smooth black-box optimization with application to policy search

no code implementations • L4DC 2020 • Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour

For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points.