11,061 research outputs found
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
Neural Architecture Search (NAS) has shown great success in automating the
design of neural networks, but the prohibitive amount of computations behind
current NAS methods requires further investigations in improving the sample
efficiency and the network evaluation cost to get better results in a shorter
time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS)
based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the
search efficiency by adaptively balancing the exploration and exploitation at
the state level, and by a Meta-Deep Neural Network (DNN) to predict network
accuracies for biasing the search toward a promising region. To amortize the
network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed
design and reduces the number of epochs in evaluating a network by transfer
learning, which is guided with the tree structure in MCTS. In 12 GPU days and
1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy
on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods
in both the accuracy and sampling efficiency. Particularly, we also evaluate
AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more
sample efficient than Random Search and Regularized Evolution in finding the
global optimum. Finally, we show the searched architecture improves a variety
of vision applications from Neural Style Transfer, to Image Captioning and
Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial
Intelligence (AAAI-2020
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Scaling Monte Carlo Tree Search on Intel Xeon Phi
Many algorithms have been parallelized successfully on the Intel Xeon Phi
coprocessor, especially those with regular, balanced, and predictable data
access patterns and instruction flows. Irregular and unbalanced algorithms are
harder to parallelize efficiently. They are, for instance, present in
artificial intelligence search algorithms such as Monte Carlo Tree Search
(MCTS). In this paper we study the scaling behavior of MCTS, on a highly
optimized real-world application, on real hardware. The Intel Xeon Phi allows
shared memory scaling studies up to 61 cores and 244 hardware threads. We
compare work-stealing (Cilk Plus and TBB) and work-sharing (FIFO scheduling)
approaches. Interestingly, we find that a straightforward thread pool with a
work-sharing FIFO queue shows the best performance. A crucial element for this
high performance is the controlling of the grain size, an approach that we call
Grain Size Controlled Parallel MCTS. Our subsequent comparing with the Xeon
CPUs shows an even more comprehensible distinction in performance between
different threading libraries. We achieve, to the best of our knowledge, the
fastest implementation of a parallel MCTS on the 61 core Intel Xeon Phi using a
real application (47 relative to a sequential run).Comment: 8 pages, 9 figure
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