1,533 research outputs found
State Drought Programs and Plans: Survey of the Western United States
Drought preparedness programs are considered a primary defense against drought hazards. This article investigates state drought programs in the western United States, including a review of drought plans and interviews with state drought officials. While nearly all states have developed drought plans and larger drought programs, the scope and depth of these programs vary widely. State programs and plans typically address monitoring, declaration and response, and communication and coordination. Yet few states conduct postdrought assessments or impact and risk assessments. Resources tend to be allocated more for drought response than mitigation. Officials emphasized not only the importance of available monitoring data, but also the need for improved information for monitoring and predicting drought. State drought officials recommended the following: (1) clear and relevant drought indicators and triggers; (2) frequent communication and coordination among state agencies, local governments, and stakeholders; (3) regularly updated drought plans; and (4) strong leadership that includes a full-time state drought coordinator
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
We study the problem of generating arbitrarily large environments to improve
the throughput of multi-robot systems. Prior work proposes Quality Diversity
(QD) algorithms as an effective method for optimizing the environments of
automated warehouses. However, these approaches optimize only relatively small
environments, falling short when it comes to replicating real-world warehouse
sizes. The challenge arises from the exponential increase in the search space
as the environment size increases. Additionally, the previous methods have only
been tested with up to 350 robots in simulations, while practical warehouses
could host thousands of robots. In this paper, instead of optimizing
environments, we propose to optimize Neural Cellular Automata (NCA) environment
generators via QD algorithms. We train a collection of NCA generators with QD
algorithms in small environments and then generate arbitrarily large
environments from the generators at test time. We show that NCA environment
generators maintain consistent, regularized patterns regardless of environment
size, significantly enhancing the scalability of multi-robot systems in two
different domains with up to 2,350 robots. Additionally, we demonstrate that
our method scales a single-agent reinforcement learning policy to arbitrarily
large environments with similar patterns. We include the source code at
\url{https://github.com/lunjohnzhang/warehouse_env_gen_nca_public}.Comment: Accepted to Advances in Neural Information Processing Systems
(NeurIPS), 202
Multi-Robot Coordination and Layout Design for Automated Warehousing
With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have
studied how MAPF algorithms can be deployed to coordinate hundreds of robots in
large automated warehouses. While most works try to improve the throughput of
such warehouses by developing better MAPF algorithms, we focus on improving the
throughput by optimizing the warehouse layout. We show that, even with
state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead
to congestion for warehouses with large numbers of robots and thus have limited
scalability. We extend existing automatic scenario generation methods to
optimize warehouse layouts. Results show that our optimized warehouse layouts
(1) reduce traffic congestion and thus improve throughput, (2) improve the
scalability of the automated warehouses by doubling the number of robots in
some cases, and (3) are capable of generating layouts with user-specified
diversity measures. We include the source code at:
https://github.com/lunjohnzhang/warehouse_env_gen_publicComment: Accepted to International Joint Conference on Artificial Intelligence
(IJCAI), 2023. The paper can be found at IJCAI 2023 proceeding at
https://www.ijcai.org/proceedings/2023/061
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Training generally capable agents that perform well in unseen dynamic
environments is a long-term goal of robot learning. Quality Diversity
Reinforcement Learning (QD-RL) is an emerging class of reinforcement learning
(RL) algorithms that blend insights from Quality Diversity (QD) and RL to
produce a collection of high performing and behaviorally diverse policies with
respect to a behavioral embedding. Existing QD-RL approaches have thus far
taken advantage of sample-efficient off-policy RL algorithms. However, recent
advances in high-throughput, massively parallelized robotic simulators have
opened the door for algorithms that can take advantage of such parallelism, and
it is unclear how to scale existing off-policy QD-RL methods to these new
data-rich regimes. In this work, we take the first steps to combine on-policy
RL methods, specifically Proximal Policy Optimization (PPO), that can leverage
massive parallelism, with QD, and propose a new QD-RL method with these
high-throughput simulators and on-policy training in mind. Our proposed
Proximal Policy Gradient Arborescence (PPGA) algorithm yields a 4x improvement
over baselines on the challenging humanoid domain.Comment: Submitted to Neurips 202
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.Comment: Accepted to AAAI 202
The Evolution of the Anopheles 16 Genomes Project
We report the imminent completion of a set of reference genome assemblies for 16 species of Anopheles mosquitoes. In addition to providing a generally useful resource for comparative genomic analyses, these genome sequences will greatly facilitate exploration of the capacity exhibited by some Anopheline mosquito species to serve as vectors for malaria parasites. A community analysis project will commence soon to perform a thorough comparative genomic investigation of these newly sequenced genomes. Completion of this project via the use of short next-generation sequence reads required innovation in both the bioinformatic and laboratory realms, and the resulting knowledge gained could prove useful for genome sequencing projects targeting other unconventional genomes
Transiting Disintegrating Planetary Debris around WD 1145+017
More than a decade after astronomers realized that disrupted planetary
material likely pollutes the surfaces of many white dwarf stars, the discovery
of transiting debris orbiting the white dwarf WD 1145+017 has opened the door
to new explorations of this process. We describe the observational evidence for
transiting planetary material and the current theoretical understanding (and in
some cases lack thereof) of the phenomenon.Comment: Invited review chapter. Accepted March 23, 2017 and published October
7, 2017 in the Handbook of Exoplanets. 15 pages, 10 figure
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