31 research outputs found
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Fish in schooling formations navigate complex flow-fields replete with
mechanical energy in the vortex wakes of their companions. Their schooling
behaviour has been associated with evolutionary advantages including collective
energy savings. How fish harvest energy from their complex fluid environment
and the underlying physical mechanisms governing energy-extraction during
collective swimming, is still unknown. Here we show that fish can improve their
sustained propulsive efficiency by actively following, and judiciously
intercepting, vortices in the wake of other swimmers. This swimming strategy
leads to collective energy-savings and is revealed through the first ever
combination of deep reinforcement learning with high-fidelity flow simulations.
We find that a `smart-swimmer' can adapt its position and body deformation to
synchronise with the momentum of the oncoming vortices, improving its average
swimming-efficiency at no cost to the leader. The results show that fish may
harvest energy deposited in vortices produced by their peers, and support the
conjecture that swimming in formation is energetically advantageous. Moreover,
this study demonstrates that deep reinforcement learning can produce navigation
algorithms for complex flow-fields, with promising implications for energy
savings in autonomous robotic swarms.Comment: 26 pages, 14 figure
Optimal sensing for fish school identification
Fish schooling implies an awareness of the swimmers for their companions. In
flow mediated environments, in addition to visual cues, pressure and shear
sensors on the fish body are critical for providing quantitative information
that assists the quantification of proximity to other swimmers. Here we examine
the distribution of sensors on the surface of an artificial swimmer so that it
can optimally identify a leading group of swimmers. We employ Bayesian
experimental design coupled with two-dimensional Navier Stokes equations for
multiple self-propelled swimmers. The follower tracks the school using
information from its own surface pressure and shear stress. We demonstrate that
the optimal sensor distribution of the follower is qualitatively similar to the
distribution of neuromasts on fish. Our results show that it is possible to
identify accurately the center of mass and even the number of the leading
swimmers using surface only information
Learning Efficient Navigation in Vortical Flow Fields
Efficient point-to-point navigation in the presence of a background flow
field is important for robotic applications such as ocean surveying. In such
applications, robots may only have knowledge of their immediate surroundings or
be faced with time-varying currents, which limits the use of optimal control
techniques for planning trajectories. Here, we apply a novel Reinforcement
Learning algorithm to discover time-efficient navigation policies to steer a
fixed-speed swimmer through an unsteady two-dimensional flow field. The
algorithm entails inputting environmental cues into a deep neural network that
determines the swimmer's actions, and deploying Remember and Forget Experience
replay. We find that the resulting swimmers successfully exploit the background
flow to reach the target, but that this success depends on the type of sensed
environmental cue. Surprisingly, a velocity sensing approach outperformed a
bio-mimetic vorticity sensing approach by nearly two-fold in success rate.
Equipped with local velocity measurements, the reinforcement learning algorithm
achieved near 100% success in reaching the target locations while approaching
the time-efficiency of paths found by a global optimal control planner.Comment: 6 pages, 6 figure
A rapid qPCR method to investigate the circulation of the yeast Wickerhamomyces anomalus in humans
The yeast Wickerhamomyces anomalus has been proposed for many biotechnological applications in the food industry. However, a number of opportunistic pathogenic strains have been reported as causative agents of nosocomial fungemia. Recognition of potentially pathogenic isolates is an important challenge for the future commercialization of this yeast. The isolation of W. anomalus from different matrices and, recently, from mosquitoes, requires further investigations into its circulation in humans. Here we present a qPCR protocol for the detection of W. anomalus in human blood samples and the results of a screening of 525 donors, including different classes of patients and healthy people
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Human anatomy, morphology, and associated diseases can be studied using
medical imaging data. However, access to medical imaging data is restricted by
governance and privacy concerns, data ownership, and the cost of acquisition,
thus limiting our ability to understand the human body. A possible solution to
this issue is the creation of a model able to learn and then generate synthetic
images of the human body conditioned on specific characteristics of relevance
(e.g., age, sex, and disease status). Deep generative models, in the form of
neural networks, have been recently used to create synthetic 2D images of
natural scenes. Still, the ability to produce high-resolution 3D volumetric
imaging data with correct anatomical morphology has been hampered by data
scarcity and algorithmic and computational limitations. This work proposes a
generative model that can be scaled to produce anatomically correct,
high-resolution, and realistic images of the human brain, with the necessary
quality to allow further downstream analyses. The ability to generate a
potentially unlimited amount of data not only enables large-scale studies of
human anatomy and pathology without jeopardizing patient privacy, but also
significantly advances research in the field of anomaly detection, modality
synthesis, learning under limited data, and fair and ethical AI. Code and
trained models are available at: https://github.com/AmigoLab/SynthAnatomy.Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 202
Remember and Forget for Experience Replay
Proceedings of the 36th International Conference on Machine Learning Experience replay (ER) is a fundamental component of off-policy deep rein- forcement learning (RL). ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficiency. However, the accuracy of such updates may dete- riorate when the policy diverges from past behaviors and can undermine the performance of ER. Many algorithms mitigate this issue by tuning hyper-parameters to slow down policy changes. An alternative is to actively enforce the similarity between policy and the experiences in the replay memory. We introduce Remember and Forget Experience Replay (ReF-ER), a novel method that can enhance RL algorithms with parameterized policies. ReF-ER (1) skips gradients computed from experiences that are too unlikely with the current policy and (2) regulates policy changes within a trust region of the replayed behaviors. We couple ReF-ER with Q-learning, determinis- tic policy gradient and off-policy gradient meth- ods. We find that ReF-ER consistently improves the performance of continuous-action, off-policy RL on fully observable benchmarks and partially observable flow control problems.ISSN:2640-349