29 research outputs found
Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials
Computational exploration of the compositional spaces of materials can
provide guidance for synthetic research and thus accelerate the discovery of
novel materials. Most approaches employ high-throughput sampling and focus on
reducing the time for energy evaluation for individual compositions, often at
the cost of accuracy. Here, we present an alternative approach focusing on
effective sampling of the compositional space. The learning algorithm PhaseBO
optimizes the stoichiometry of the potential target material while improving
the probability of and accelerating its discovery without compromising the
accuracy of energy evaluation
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search
Robotic solutions for quick disaster response are essential to ensure minimal
loss of life, especially when the search area is too dangerous or too vast for
human rescuers. We model this problem as an asynchronous multi-agent
active-search task where each robot aims to efficiently seek objects of
interest (OOIs) in an unknown environment. This formulation addresses the
requirement that search missions should focus on quick recovery of OOIs rather
than full coverage of the search region. Previous approaches fail to accurately
model sensing uncertainty, account for occlusions due to foliage or terrain, or
consider the requirement for heterogeneous search teams and robustness to
hardware and communication failures. We present the Generalized
Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these
issues and is suitable for deployment on heterogeneous multi-robot systems for
active search in large unstructured environments. We show through simulation
experiments that GUTS consistently outperforms existing methods such as
parallelized Thompson Sampling and exhaustive search, recovering all OOIs in
80% of all runs. In contrast, existing approaches recover all OOIs in less than
40% of all runs. We conduct field tests using our multi-robot system in an
unstructured environment with a search area of approximately 75,000 sq. m. Our
system demonstrates robustness to various failure modes, achieving full
recovery of OOIs (where feasible) in every field run, and significantly
outperforming our baseline.Comment: 7 pages, 5 figures, 1 table, for associated video see:
https://youtu.be/K0jkzdQ_j2E , to appear in International Conference on
Robotics and Automation (ICRA) 202
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
We present a new type of acquisition functions for online decision making in
multi-armed and contextual bandit problems with extreme payoffs. Specifically,
we model the payoff function as a Gaussian process and formulate a novel type
of upper confidence bound (UCB) acquisition function that guides exploration
towards the bandits that are deemed most relevant according to the variability
of the observed rewards. This is achieved by computing a tractable likelihood
ratio that quantifies the importance of the output relative to the inputs and
essentially acts as an \textit{attention mechanism} that promotes exploration
of extreme rewards. We demonstrate the benefits of the proposed methodology
across several synthetic benchmarks, as well as a realistic example involving
noisy sensor network data. Finally, we provide a JAX library for efficient
bandit optimization using Gaussian processes.Comment: 10 pages, 4 figures, 1 tabl
Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network
Fast charging has attracted increasing attention from the battery community
for electrical vehicles (EVs) to alleviate range anxiety and reduce charging
time for EVs. However, inappropriate charging strategies would cause severe
degradation of batteries or even hazardous accidents. To optimize fast-charging
strategies under various constraints, particularly safety limits, we propose a
novel deep Bayesian optimization (BO) approach that utilizes Bayesian recurrent
neural network (BRNN) as the surrogate model, given its capability in handling
sequential data. In addition, a combined acquisition function of expected
improvement (EI) and upper confidence bound (UCB) is developed to better
balance the exploitation and exploration. The effectiveness of the proposed
approach is demonstrated on the PETLION, a porous electrode theory-based
battery simulator. Our method is also compared with the state-of-the-art BO
methods that use Gaussian process (GP) and non-recurrent network as surrogate
models. The results verify the superior performance of the proposed fast
charging approaches, which mainly results from that: (i) the BRNN-based
surrogate model provides a more precise prediction of battery lifetime than
that based on GP or non-recurrent network; and (ii) the combined acquisition
function outperforms traditional EI or UCB criteria in exploring the optimal
charging protocol that maintains the longest battery lifetime
Regret Bounds for Noise-Free Bayesian Optimization
Bayesian optimisation is a powerful method for non-convex black-box
optimization in low data regimes. However, the question of establishing tight
upper bounds for common algorithms in the noiseless setting remains a largely
open question. In this paper, we establish new and tightest bounds for two
algorithms, namely GP-UCB and Thompson sampling, under the assumption that the
objective function is smooth in terms of having a bounded norm in a Mat\'ern
RKHS. Importantly, unlike several related works, we do not consider perfect
knowledge of the kernel of the Gaussian process emulator used within the
Bayesian optimization loop. This allows us to provide results for practical
algorithms that sequentially estimate the Gaussian process kernel parameters
from the available data