665 research outputs found
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
Tuning machine parameters of particle accelerators is a repetitive and
time-consuming task, that is challenging to automate. While many off-the-shelf
optimization algorithms are available, in practice their use is limited because
most methods do not account for safety-critical constraints that apply to each
iteration, including loss signals or step-size limitations. One notable
exception is safe Bayesian optimization, which is a data-driven tuning approach
for global optimization with noisy feedback. We propose and evaluate a step
size-limited variant of safe Bayesian optimization on two research faculties of
the Paul Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL)
and b) the High-Intensity Proton Accelerator (HIPA). We report promising
experimental results on both machines, tuning up to 16 parameters subject to
more than 200 constraints
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Expected Improvement (EI) is arguably the most popular acquisition function
in Bayesian optimization and has found countless successful applications, but
its performance is often exceeded by that of more recent methods. Notably, EI
and its variants, including for the parallel and multi-objective settings, are
challenging to optimize because their acquisition values vanish numerically in
many regions. This difficulty generally increases as the number of
observations, dimensionality of the search space, or the number of constraints
grow, resulting in performance that is inconsistent across the literature and
most often sub-optimal. Herein, we propose LogEI, a new family of acquisition
functions whose members either have identical or approximately equal optima as
their canonical counterparts, but are substantially easier to optimize
numerically. We demonstrate that numerical pathologies manifest themselves in
"classic" analytic EI, Expected Hypervolume Improvement (EHVI), as well as
their constrained, noisy, and parallel variants, and propose corresponding
reformulations that remedy these pathologies. Our empirical results show that
members of the LogEI family of acquisition functions substantially improve on
the optimization performance of their canonical counterparts and surprisingly,
are on par with or exceed the performance of recent state-of-the-art
acquisition functions, highlighting the understated role of numerical
optimization in the literature.Comment: NeurIPS 2023 Spotligh
Model-based relative entropy stochastic search
Stochastic search algorithms are general black-box optimizers. Due to their ease
of use and their generality, they have recently also gained a lot of attention in operations
research, machine learning and policy search. Yet, these algorithms require
a lot of evaluations of the objective, scale poorly with the problem dimension, are
affected by highly noisy objective functions and may converge prematurely. To
alleviate these problems, we introduce a new surrogate-based stochastic search
approach. We learn simple, quadratic surrogate models of the objective function.
As the quality of such a quadratic approximation is limited, we do not greedily exploit
the learned models. The algorithm can be misled by an inaccurate optimum
introduced by the surrogate. Instead, we use information theoretic constraints to
bound the ‘distance’ between the new and old data distribution while maximizing
the objective function. Additionally the new method is able to sustain the exploration
of the search distribution to avoid premature convergence. We compare our
method with state of art black-box optimization methods on standard uni-modal
and multi-modal optimization functions, on simulated planar robot tasks and a
complex robot ball throwing task. The proposed method considerably outperforms
the existing approaches
Learning to represent surroundings, anticipate motion and take informed actions in unstructured environments
Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly
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