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ShareScore release 0.9.0
Dataset results
22 results for “deep reinforcement learning”
Deep reinforcement learning for the control of microbial co-cultures in bioreactors
<p>Data for the figures in the paper:<br> <a href="https://www.biorxiv.org/content/10.1101/457366v2">https://www.biorxiv.org/content/10.1101/457366v2</a><br> (in press PLoS Comp Biol.)</p> <p>Abstract:<br> Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence – reinforcement learning – for the control of co-cultures within continuous bioreactors. We confirm that feedback via reinforcement learning can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.</p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
Source codes and datasets for the paper "DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning"
<p>This contains the<strong> reproducible source code and dataset </strong>for the paper "DRLComplex : Reconstruction of protein quaternary structures using deep reinforcement learning paper"</p>
Dataset for "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"
<p>Dataset containing the results shown in Fig. 6 of "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark".</p>
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography
<p>These are the data sets used for the publication https://arxiv.org/abs/2203.15413.</p> <p>In more detail, the DataFile.pkl files in the training_data and testing_data folders are the actual data, including diffraction patterns, the corresponding reconstructions and the used illumination probe. The folders dwcnt_training_data and dwcnt_testing_data include the data sets of the simulated double-walled carbon nanotube.</p> <p>The reconstruction folder includes the source code of the reconstruction algorithm ROP and the used parameter files.</p> <p>The results folder includes the updated weights of the network model and the reconstructed potentials for the 25 test data sets used in the comparison of the publication.</p> <p>The sequence_grid.npy file stores the sparse grid scanning sequence used for initialization and comparison as described in the publication. The dwcnt_sequence_grid.npy file is the corresponding file for the double-walled carbon nanotube data.</p> <p>If you want more information, please contact the corresponding author of the publication, at schlozma@hu-berlin.de. </p>
Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening
<p>This compressed file contains all datasets made for the validation of MUBDsyn.</p><ul><li>datasets_int_val: 17 cases in this folder are derived from <a href="https://github.com/jwxia2014/ULS-UDS">MUBD for GPCRs</a>. MUBDreal was made by <a href="https://github.com/jwxia2014/MUBD-DecoyMaker2.0">MUBD-DecoyMaker2.0</a> and MUBDsyn was made by <a href="https://github.com/taoshen99/MUBDsyn">MUBD-DecoyMakersyn</a>.</li><li>datasets_ext_val_classical_VS: Five cases in this folder are derived from the shared cases of MUV and DUD-E. The active sets of MUV were taken as the input to make corresponding MUBD datasets. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_SI_classical_VS: DeepCoy and TocoDecoy were used to make the datasets corresponding to the same five cases above. The data of DeepCoy was directly retrieved from <a href="https://opig.stats.ox.ac.uk/resources">DeepCoy resources at OPIG</a> while topology decoys of TocoDecoy_9W were made based on the scripts provided at <a href="https://github.com/5AGE-zhang/TocoDecoy">TocoDecoy GitHub Repository</a>. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_ML_VS: Ten cases in this folder are derived from <a href="http://nrlist.drugdesign.fr/">NRLiSt-BDB</a>. Corresponding MUBD datasets were made as described above.</li></ul><p>All these datasets can be used for the reproduction of validation performed in the manuscript or to benchmark various virtual screening methods.</p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
The AI Economist: Taxation policy design via two-level deep reinforcement learning
<p>This dataset contains all raw experimental data for the paper "The AI Economist: Taxation Policy Design via Two-level Deep Multi-Agent Reinforcement Learning". </p> <p>The accompanying simulation, reinforcement learning, and data visualization code can be found at https://github.com/salesforce/ai-economist.</p> <p>For the one-step economy experiments, we provide:</p> <ul> <li> <p>training histories,</p> </li> <li> <p>configuration files (these experiments do not use phases), and</p> </li> <li> <p>final agent and planner models.</p> </li> </ul> <p>For the Gather-Trade-Build scenario, the data covers 6 spatial layouts: two Open-Quadrant (with 4 and 10 agents), and four Split-World maps with different configurations of the high-skilled and low-skilled agents. It also covers 4 tax policies (the AI Economist, Saez, free-market, and US federal). In addition, the AI Economist has been optimized for two social welfare functions: the product of equality and productivity, and inverse-income weighted utility. The Saez tax policy also uses estimated elasticities. </p> <p>Each experiment was repeated with different random seeds: 10 seeds for the Open-Quadrant scenarios, and 5 seeds for the Split-World scenarios. For each individual experiment, we provide: </p> <ul> <li> <p>Training histories (e.g. equality and productivity throughout training)</p> </li> <li> <p>the phase 1 and phase 2 configuration files, </p> </li> <li> <p>40 episode dense logs (the final 10 simulation logs across 4 environment replicas),</p> </li> <li> <p>phase 1 final agent models, and</p> </li> <li> <p>phase 2 final agent and planner models.</p> </li> </ul> <p>Finally, we include all data and results used to calibrate the Saez elasticity estimates and to estimate elasticity directly from a sweep over flat-rate tax policies:</p> <ul> <li> <p>training histories,</p> </li> <li> <p>the phase 1 and phase 2 configuration files, </p> </li> <li> <p>phase 1 final agent models, and</p> </li> <li> <p>phase 2 final agent and planner models.</p> </li> </ul>
Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems
<p>Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6's State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters.</p>
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
Policies used in "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"
<p>Policies used in the paper "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark". They can be tested using the software "OTTO-benchmark" available at https://github.com/auroreloisy/otto-benchmark.</p>
YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
<p>This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.</p>
The AI Economist: Taxation policy design via two-level deep reinforcement learning
Open the record for dataset details and reuse information.
Supplementary video of the paper "Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach"
<p>Supplementary video of the paper "Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach" showing the experimental results.</p>
Automated inference of sequences of operations in RESTful APIs with deep reinforcement learning - replication package
<p>This directory contains the replication package of the article entitled "Automated inference of sequences of operations in RESTful APIs with deep reinforcement learning". Please note that this is NOT a real publication or scientific report, but a project created for the purpose of learning the basic concepts of research by emulating the process of creating an article.</p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" accepted to the International Conference on Software Testing (ICST) 2023. The replication package is at https://github.com/FlowSs/RLMutation.</p>
Champion-Level Drone Racing using Deep Reinforcement Learning: Supplementary Data
<p>Supplementary data for the publication "Champion-Level Drone Racing using Deep Reinforcement Learning".</p> <p>racing_data.zip: The data includes the recorded position of human and autonomous drones during head-to-head races and time-trial runs. The included code provides plotting and statistical analysis of the data.</p> <p>pseudocode.zip: Pseudocode that illustrates gate detection, VIO drift estimation, policy training, and residual model identification.</p>
Data and code from: Deep reinforcement learning for pressure optimization in water distribution networks with multiple pumping stations: Case study
Open the record for dataset details and reuse information.
Track-to-Learn: A general framework for tractography with deep reinforcement learning
<p>Datasets used in the experiments presented in Track-to-Learn: A general framework for tractography using deep reinforcement learning</p> <p>See https://github.com/scil-vital/TrackToLearn to use the datasets and recreate the experiments.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.