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Generalizing soft actor-critic algorithms to discrete action spaces | ATARI is a suite of video games used by reinforcement learning (RL)
researchers to test the effectiveness of the learning algorithm. Receiving only
the raw pixels and the game score, the agent learns to develop sophisticated
strategies, even to the comparable level of a professional human games tester.
Ideally, we also want an agent requiring very few interactions with the
environment. Previous competitive model-free algorithms for the task use the
valued-based Rainbow algorithm without any policy head. In this paper, we
change it by proposing a practical discrete variant of the soft actor-critic
(SAC) algorithm. The new variant enables off-policy learning using policy heads
for discrete domains. By incorporating it into the advanced Rainbow variant,
i.e., the ``bigger, better, faster'' (BBF), the resulting SAC-BBF improves the
previous state-of-the-art interquartile mean (IQM) from 1.045 to 1.088, and it
achieves these results using only replay ratio (RR) 2. By using lower RR 2, the
training time of SAC-BBF is strictly one-third of the time required for BBF to
achieve an IQM of 1.045 using RR 8. As a value of IQM greater than one
indicates super-human performance, SAC-BBF is also the only model-free
algorithm with a super-human level using only RR 2. The code is publicly
available on GitHub at https://github.com/lezhang-thu/bigger-better-faster-SAC. | [
"Le Zhang",
"Yong Gu",
"Xin Zhao",
"Yanshuo Zhang",
"Shu Zhao",
"Yifei Jin",
"Xinxin Wu"
] | [
"IQM"
] | "2024-07-08T03:20:45Z" | 2407.11044v1 |
Adapting Pretrained Networks for Image Quality Assessment on High
Dynamic Range Displays | Conventional image quality metrics (IQMs), such as PSNR and SSIM, are
designed for perceptually uniform gamma-encoded pixel values and cannot be
directly applied to perceptually non-uniform linear high-dynamic-range (HDR)
colors. Similarly, most of the available datasets consist of
standard-dynamic-range (SDR) images collected in standard and possibly
uncontrolled viewing conditions. Popular pre-trained neural networks are
likewise intended for SDR inputs, restricting their direct application to HDR
content. On the other hand, training HDR models from scratch is challenging due
to limited available HDR data. In this work, we explore more effective
approaches for training deep learning-based models for image quality assessment
(IQA) on HDR data. We leverage networks pre-trained on SDR data (source domain)
and re-target these models to HDR (target domain) with additional fine-tuning
and domain adaptation. We validate our methods on the available HDR IQA
datasets, demonstrating that models trained with our combined recipe outperform
previous baselines, converge much quicker, and reliably generalize to HDR
inputs. | [
"Andrei Chubarau",
"Hyunjin Yoo",
"Tara Akhavan",
"James Clark"
] | [
"IQM"
] | "2024-05-01T17:57:12Z" | 2405.00670v1 |
IQMDose3D: a software tool for reconstructing the dose in patient using
patient planning CT images and the signals measured by IQM detector | The integral quality monitor (IQM) system compares the signal measured with a
large volume chamber mounted to the linear accelerator's head to the signal
calculated using the patient DICOM RT plan for patient-specific quality
assurance (PSQA). A method was developed to reconstruct the dose in patients
using the signal measured by IQM chamber and patient planning CT images. A
software tool named IQMDose3D was implemented to automate this procedure and
integrated into the IQM-based PSQA workflow. IQMDose3D enables the physicists
to evaluate PSQA by focusing on the clinical perspective by comparing the
delivered plan to the approved clinical plan in terms of the clinical goals,
dose-volume histogram (DVH) in addition to the three-dimensional (3D) gamma map
and gamma pass rate. | [
"Aitang Xing",
"Gary Goozee",
"Alison Gray",
"Vaughan Moutrie",
"Sankar Arumugam",
"Shrikant Deshpande",
"Anthony Espinoza",
"Vasilis Kondilis",
"Marjorie McDonald",
"Philip Vial"
] | [
"IQM"
] | "2024-03-26T05:24:19Z" | 2403.17394v2 |
Adaptive Feature Selection for No-Reference Image Quality Assessment by
Mitigating Semantic Noise Sensitivity | The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA)
methods typically rely on feature extraction from upstream semantic backbone
networks, assuming that all extracted features are relevant. However, we make a
key observation that not all features are beneficial, and some may even be
harmful, necessitating careful selection. Empirically, we find that many image
pairs with small feature spatial distances can have vastly different quality
scores, indicating that the extracted features may contain a significant amount
of quality-irrelevant noise. To address this issue, we propose a Quality-Aware
Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective
to remove harmful semantic noise features from the upstream task. Specifically,
QFM-IQM enhances the semantic noise distinguish capabilities by matching image
pairs with similar quality scores but varying semantic features as adversarial
semantic noise and adaptively adjusting the upstream task's features by
reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize
a distillation framework to expand the dataset and improve the model's
generalization ability. Our approach achieves superior performance to the
state-of-the-art NR-IQA methods on eight standard IQA datasets. | [
"Xudong Li",
"Timin Gao",
"Runze Hu",
"Yan Zhang",
"Shengchuan Zhang",
"Xiawu Zheng",
"Jingyuan Zheng",
"Yunhang Shen",
"Ke Li",
"Yutao Liu",
"Pingyang Dai",
"Rongrong Ji"
] | [
"IQM"
] | "2023-12-11T06:50:27Z" | 2312.06158v2 |
Augmenting Unsupervised Reinforcement Learning with Self-Reference | Humans possess the ability to draw on past experiences explicitly when
learning new tasks and applying them accordingly. We believe this capacity for
self-referencing is especially advantageous for reinforcement learning agents
in the unsupervised pretrain-then-finetune setting. During pretraining, an
agent's past experiences can be explicitly utilized to mitigate the
nonstationarity of intrinsic rewards. In the finetuning phase, referencing
historical trajectories prevents the unlearning of valuable exploratory
behaviors. Motivated by these benefits, we propose the Self-Reference (SR)
approach, an add-on module explicitly designed to leverage historical
information and enhance agent performance within the pretrain-finetune
paradigm. Our approach achieves state-of-the-art results in terms of
Interquartile Mean (IQM) performance and Optimality Gap reduction on the
Unsupervised Reinforcement Learning Benchmark for model-free methods, recording
an 86% IQM and a 16% Optimality Gap. Additionally, it improves current
algorithms by up to 17% IQM and reduces the Optimality Gap by 31%. Beyond
performance enhancement, the Self-Reference add-on also increases sample
efficiency, a crucial attribute for real-world applications. | [
"Andrew Zhao",
"Erle Zhu",
"Rui Lu",
"Matthieu Lin",
"Yong-Jin Liu",
"Gao Huang"
] | [
"IQM"
] | "2023-11-16T09:07:34Z" | 2311.09692v1 |
Deep Reinforcement Learning for Autonomous Spacecraft Inspection using
Illumination | This paper investigates the problem of on-orbit spacecraft inspection using a
single free-flying deputy spacecraft, equipped with an optical sensor, whose
controller is a neural network control system trained with Reinforcement
Learning (RL). This work considers the illumination of the inspected spacecraft
(chief) by the Sun in order to incentivize acquisition of well-illuminated
optical data. The agent's performance is evaluated through statistically
efficient metrics. Results demonstrate that the RL agent is able to inspect all
points on the chief successfully, while maximizing illumination on inspected
points in a simulated environment, using only low-level actions. Due to the
stochastic nature of RL, 10 policies were trained using 10 random seeds to
obtain a more holistic measure of agent performance. Over these 10 seeds, the
interquartile mean (IQM) percentage of inspected points for the finalized model
was 98.82%. | [
"David van Wijk",
"Kyle Dunlap",
"Manoranjan Majji",
"Kerianne L. Hobbs"
] | [
"IQM"
] | "2023-08-04T23:43:53Z" | 2308.02743v1 |
Enhancing image quality prediction with self-supervised visual masking | Full-reference image quality metrics (FR-IQMs) aim to measure the visual
differences between a pair of reference and distorted images, with the goal of
accurately predicting human judgments. However, existing FR-IQMs, including
traditional ones like PSNR and SSIM and even perceptual ones such as HDR-VDP,
LPIPS, and DISTS, still fall short in capturing the complexities and nuances of
human perception. In this work, rather than devising a novel IQM model, we seek
to improve upon the perceptual quality of existing FR-IQM methods. We achieve
this by considering visual masking, an important characteristic of the human
visual system that changes its sensitivity to distortions as a function of
local image content. Specifically, for a given FR-IQM metric, we propose to
predict a visual masking model that modulates reference and distorted images in
a way that penalizes the visual errors based on their visibility. Since the
ground truth visual masks are difficult to obtain, we demonstrate how they can
be derived in a self-supervised manner solely based on mean opinion scores
(MOS) collected from an FR-IQM dataset. Our approach results in enhanced FR-IQM
metrics that are more in line with human prediction both visually and
quantitatively. | [
"Uğur Çoğalan",
"Mojtaba Bemana",
"Hans-Peter Seidel",
"Karol Myszkowski"
] | [
"IQM"
] | "2023-05-31T13:48:51Z" | 2305.19858v2 |
Quantum Speedups for Bayesian Network Structure Learning | The Bayesian network structure learning (BNSL) problem asks for a directed
acyclic graph that maximizes a given score function. For networks with $n$
nodes, the fastest known algorithms run in time $O(2^n n^2)$ in the worst case,
with no improvement in the asymptotic bound for two decades. Inspired by recent
advances in quantum computing, we ask whether BNSL admits a polynomial quantum
speedup, that is, whether the problem can be solved by a quantum algorithm in
time $O(c^n)$ for some constant $c$ less than $2$. We answer the question in
the affirmative by giving two algorithms achieving $c \leq 1.817$ and $c \leq
1.982$ assuming the number of potential parent sets is, respectively,
subexponential and $O(1.453^n)$. Both algorithms assume the availability of a
quantum random access memory. | [
"Juha Harviainen",
"Kseniya Rychkova",
"Mikko Koivisto"
] | [] | "2023-05-31T09:15:28Z" | 2305.19673v1 |
Efficient Offline Policy Optimization with a Learned Model | MuZero Unplugged presents a promising approach for offline policy learning
from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned
model and leverages Reanalyze algorithm to learn purely from offline data. For
good performance, MCTS requires accurate learned models and a large number of
simulations, thus costing huge computing time. This paper investigates a few
hypotheses where MuZero Unplugged may not work well under the offline RL
settings, including 1) learning with limited data coverage; 2) learning from
offline data of stochastic environments; 3) improperly parameterized models
given the offline data; 4) with a low compute budget. We propose to use a
regularized one-step look-ahead approach to tackle the above issues. Instead of
planning with the expensive MCTS, we use the learned model to construct an
advantage estimation based on a one-step rollout. Policy improvements are
towards the direction that maximizes the estimated advantage with
regularization of the dataset. We conduct extensive empirical studies with
BSuite environments to verify the hypotheses and then run our algorithm on the
RL Unplugged Atari benchmark. Experimental results show that our proposed
approach achieves stable performance even with an inaccurate learned model. On
the large-scale Atari benchmark, the proposed method outperforms MuZero
Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e.,
1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM
normalized score with the same hardware and software stacks. Our implementation
is open-sourced at https://github.com/sail-sg/rosmo. | [
"Zichen Liu",
"Siyi Li",
"Wee Sun Lee",
"Shuicheng Yan",
"Zhongwen Xu"
] | [
"IQM"
] | "2022-10-12T07:41:04Z" | 2210.05980v2 |
Recent Developments in Quantum-Circuit Refrigeration | We review the recent progress in direct active cooling of the
quantum-electric degrees freedom in engineered circuits, or quantum-circuit
refrigeration. In 2017, the invention of a quantum-circuit refrigerator (QCR)
based on photon-assisted tunneling of quasiparticles through a
normal-metal--insulator--superconductor junction inspired a series of
experimental studies demonstrating the following main properties: (i) the
direct-current (dc) bias voltage of the junction can change the QCR-induced
damping rate of a superconducting microwave resonator by orders of magnitude
and give rise to non-trivial Lamb shifts, (ii) the damping rate can be
controlled in nanosecond time scales, and (iii) the dc bias can be replaced by
a microwave excitation, the amplitude of which controls the induced damping
rate. Theoretically, it is predicted that state-of-the-art superconducting
resonators and qubits can be reset with an infidelity lower than $10^{-4}$ in
tens of nanoseconds using experimentally feasible parameters. A QCR-equipped
resonator has also been demonstrated as an incoherent photon source with an
output temperature above one kelvin yet operating at millikelvin. This source
has been used to calibrate cryogenic amplification chains. In the future, the
QCR may be experimentally used to quickly reset superconducting qubits, and
hence assist in the great challenge of building a practical quantum computer. | [
"Timm Fabian Mörstedt",
"Arto Viitanen",
"Vasilii Vadimov",
"Vasilii Sevriuk",
"Matti Partanen",
"Eric Hyyppä",
"Gianluigi Catelani",
"Matti Silveri",
"Kuan Yen Tan",
"Mikko Möttönen"
] | [] | "2021-11-22T14:27:26Z" | 2111.11234v1 |
Correlation between image quality metrics of magnetic resonance images
and the neural network segmentation accuracy | Deep neural networks with multilevel connections process input data in
complex ways to learn the information.A networks learning efficiency depends
not only on the complex neural network architecture but also on the input
training images.Medical image segmentation with deep neural networks for skull
stripping or tumor segmentation from magnetic resonance images enables learning
both global and local features of the images.Though medical images are
collected in a controlled environment,there may be artifacts or equipment based
variance that cause inherent bias in the input set.In this study, we
investigated the correlation between the image quality metrics of MR images
with the neural network segmentation accuracy.For that we have used the 3D
DenseNet architecture and let the network trained on the same input but
applying different methodologies to select the training data set based on the
IQM values.The difference in the segmentation accuracy between models based on
the random training inputs with IQM based training inputs shed light on the
role of image quality metrics on segmentation accuracy.By running the image
quality metrics to choose the training inputs,further we may tune the learning
efficiency of the network and the segmentation accuracy. | [
"Rajarajeswari Muthusivarajan",
"Adrian Celaya",
"Joshua P. Yung",
"Satish Viswanath",
"Daniel S. Marcus",
"Caroline Chung",
"David Fuentes"
] | [
"IQM"
] | "2021-11-01T17:02:34Z" | 2111.01093v1 |
Stellar Structure and Stability of Charged Interacting Quark Stars and
Their Scaling Behaviour | We explore the stellar structure and radial stability of charged quark stars
composed of interacting quark matter (IQM) in three classes of commonly used
charge models. We adopt a general parametrization of IQM equation of state that
includes the corrections from perturbative QCD, color superconductivity, and
the strange quark mass into one parameter $\lambda$, or one dimensionless
parameter $\bar{\lambda}=\lambda^2/(4B_{\rm eff})$ after being rescaled with
the effective bag constant $B_{\rm eff}$. We find that increasing charge tends
to increase the mass and radius profiles, and enlarges the separation size in
mass between the maximum mass point and the point where zero eigenfrequencies
$\omega^2_0=0$ of the fundamental radial oscillation mode occur. The sign of
the separation in central density depends on the charge model; this separation
also has a dependence on $\lambda$ such that increasing $\lambda$ (which can
occur for either large color superconductivity or small strange quark mass)
tends to decrease this separation size for the first and third classes of
charge models monotonically. Moreover, for the second and third classes of
charge models, we manage to numerically and analytically identify a new kind of
stellar structure with a zero central pressure but a finite radius and mass.
All the calculations and analysis are performed in a general dimensionless
rescaling approach so that the results are independent of explicit values of
dimensional parameters. | [
"Chen Zhang",
"Michael Gammon",
"Robert B. Mann"
] | [
"IQM"
] | "2021-08-31T16:54:59Z" | 2108.13972v2 |
Gravitational wave echoes from interacting quark stars | We show that interacting quark stars (IQSs) composed of interacting quark
matter (IQM), including the strong interaction effects such as perturbative QCD
corrections and color superconductivity, can be compact enough to feature a
photon sphere that is essential to the signature of gravitational wave echoes.
We utilize an IQM equation of state unifying all interacting phases by a simple
reparametrization and rescaling, through which we manage to maximally reduce
the number of degrees of freedom into one dimensionless parameter
$\bar{\lambda}$ that characterizes the relative size of strong interaction
effects. It turns out that gravitational wave echoes are possible for IQSs with
$\bar{\lambda}\gtrsim10$ at large center pressure. Rescaling the dimension
back, we illustrate its implication on the dimensional parameter space of
effective bag constant $B_{\rm eff}$ and the superconducting gap $\Delta$ with
variations of the perturbative QCD parameter $a_4$ and the strange quark mass
$m_s$. We calculate the rescaled GW echo frequencies $\bar{f}_\text{echo}$
associated with IQSs, from which we obtain a simple scaling relation for the
minimal echo frequency $f_\text{echo}^{\rm min}\approx 5.76 {\sqrt{B_{\rm
eff}/\text{(100 MeV)}^4}} \,\,\, \rm kHz$ at the large $\bar{\lambda}$ limit. | [
"Chen Zhang"
] | [
"IQM"
] | "2021-07-20T17:39:28Z" | 2107.09654v2 |
Unified Interacting Quark Matter and its Astrophysical Implications | We investigate interacting quark matter (IQM), including the perturbative QCD
correction and color superconductivity, for both up-down quark matter ($ud$QM)
and strange quark matter (SQM). We first derive an equation of state (EOS)
unifying all cases by a simple reparametrization and rescaling, through which
we manage to maximally reduce the number of degrees of freedom. We find, in
contrast to the conventional EOS $p=1/3(\rho-4B_{\rm eff})$ for non-interacting
quark matter, that taking the extreme strongly interacting limit on the unified
IQM EOS gives $p=\rho-2B_{\rm eff}$, where $B_{\rm eff}$ is the effective bag
constant. We employ the unified EOS to explore the properties of pure
interacting quark stars (IQSs) composed of IQM. We describe how recent
astrophysical observations, such as the pulsar-mass measurements, the NICER
analysis, and the binary merger gravitational-wave events GW170817, GW190425,
and GW190814, further constrain the parameter space. An upper bound for the
maximum allowed mass of IQSs is found to be $M_{\rm TOV}\lesssim 3.23
M_{\odot}$. Our analysis indicates a new possibility that the currently
observed compact stars, including the recently reported GW190814's secondary
component ($M=2.59^{+0.08}_{-0.09}\, M_{\odot}$), can be quark stars composed
of interacting quark matter. | [
"Chen Zhang",
"Robert B. Mann"
] | [
"IQM"
] | "2020-09-15T15:45:16Z" | 2009.07182v3 |
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