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Reinforcement Learning

Offline RL trajectories, game data, robot demonstrations, RLHF, multi-agent interaction

11,130 datasets

Draft Programme for Government Framework 2016-21 with 14 Strategic Outcomes

A draft government framework document containing 14 strategic outcomes for societal wellbeing, supported by 42 performance indicators. The document was agreed by the Executive on 26 May 2016 and is published by OpenDataNI under the OGL-UK-3.0 license. It is available as a PDF file, last updated on the platform in July 2026.

TextStrategic OutcomesPerformance IndicatorsHealthcareFinanceGovernment PolicyPublic Sector+1

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Reinforcement Learning

UK Local Authority Housing Statistics on Dwellings, Lettings, and Homelessness

The Housing Strategy Statistical Appendix (HSSA) is a policy-oriented statistical return submitted annually by UK local authorities. It covers a wide range of housing tenure information, including dwelling stock, vacancies, waiting lists, lettings, homelessness, and houses in multiple occupation. The dataset is provided by the Ministry of Housing, Communities and Local Government and was last updated on July 8, 2026.

TabularExcelDwelling StockPolicy DataHousing StatisticsFinanceLocal Authority+1

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Reinforcement Learning

NESP TWQ Project 5.9: Gully Remediation Effectiveness in the Burdekin Catchment, 2019-2020

Five gully remediation trial sites in the Burdekin catchment provide measured data to evaluate a significant government investment of approximately $50 million in water quality improvement. CSIRO collected land condition, terrain, and water quality data to demonstrate whether on-ground programs improve water quality and quantify the cost-effectiveness of various approaches. The project used Lidar to extrapolate field data, supporting the prioritization of future remediation sites at larger scales.

GeospatialCost EffectivenessGully ErosionLidar ExtrapolationBenchmarkWater QualityLand Remediation+1

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Reinforcement Learning

Productivity Measures: Supporting Material for UK Economic Releases

UK productivity data compiled by the Office for National Statistics as supporting material for official releases. The dataset is licensed under OGL-UK-3.0 and was last updated on 2026-07-08. Its specific contents and structure are described as supporting material.

Tabular🇬🇧 United KingdomEconomic IndicatorsONSProductivity+1

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Reinforcement Learning

UK Biodiversity Indicator A1: Public Awareness and Support for Conservation, 2014

Survey data for the UK biodiversity indicator A1 measures public awareness, understanding, and support for conservation. The indicator uses a hierarchical system to group people based on their awareness of threats, concern about biodiversity loss, and actions taken to protect it. It is one of 24 indicators published by the Joint Nature Conservation Committee on behalf of Defra, with the latest data from 2014.

Tabular🇬🇧 United KingdomSurvey DataBiodiversity IndicatorsPublic AwarenessConservation Support+1

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Reinforcement Learning

Seafloor Geomorphology of the Australian Margin and Adjacent Seafloor at 250m Resolution

For the first time, the distribution of seafloor geomorphic features has been systematically mapped over 48.9 million km2 of the Australian margin and adjacent seafloor. The dataset, from the Australian Ocean Data Network, identifies 21 feature types using a 250 m spatial resolution bathymetry model, resulting in 6702 individual geomorphic features. The mapped area includes the seafloor surrounding the Australian mainland and the island territories of Christmas, Cocos (Keeling), Macquarie and Norfolk Islands.

GeospatialSeafloor GeomorphologySeafloorGeomorphologyLarge ScaleMarine GeologyAustralian MarginBathymetry+1

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Reinforcement Learning

RRAP ECT 2.1: Coral Larval Heat Tolerance Selection Experiment

162 coral seeding devices containing larvae from three thermal selection treatments were deployed across nine sites at Davies Reef. The dataset documents a field-based experiment testing the artificial selection of Acropora spathulata coral larval heat tolerance for restoration purposes. It was published via the Australian Ocean Data Network in June 2026.

TabularCoral Reef RestorationLarge ScaleThermal ToleranceArtificial selectionField ExperimentSyntheticMarine Biology+1

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Reinforcement Learning

Predicted Seabed Sediment Content in the Timor Sea Region, 10m Resolution

Spatially continuous raster data predicts seabed gravel, mud, and sand content as weight percentages across eight areas in the Australian continental EEZ's Timor Sea region. The dataset supersedes previous predictions with demonstrated accuracy improvements, achieving a VEcv of 71% for mud, 72% for sand, and 42% for gravel. This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub.

GeospatialZIPTimor SeaMarine SedimentAustralian EezSeabed Composition+1

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Reinforcement Learning

East Antarctic Sediment Grain Size and Composition Data from 1950s to 2012

472 sediment grain size samples and 304 biogenic silica samples provide insight into seabed habitats in East Antarctica. This compilation from the Australian Ocean Data Network incorporates data collected from the 1950s to 2012. Sedimentary properties are overlain on bathymetry maps to illustrate habitat variations within proposed Marine Protected Areas.

TabularGeospatialExcelBenthic HabitatAntarctic SedimentsEnvironmental DataMarine Geology+1

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Reinforcement Learning

000VIC Emergency Markers: Geospatial Reference Points for Melbourne

Metropolitan Melbourne's network of emergency markers provides precise location identification for Triple Zero (000) callers. Each green-and-white sign, featuring a three-letter and three-number code, is linked to GPS coordinates and an internal dataset of access instructions. This system enables emergency service operators to direct police, fire, ambulance, and VICSES units efficiently, advising on obstructions like locked gates or road closures.

TabularGeospatialCSVJSONTelecommunicationsGeospatial LocationsPublic SafetyEmergency MarkersEmergency Response+1

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Reinforcement Learning

GEOMAT: Australian Continental Shelf Sediment Mobility Models

Australian continental shelf sediment mobility is modelled using tidal and swell-wave energy. The project predicts that swell waves mobilized fine sand over 63.5% of the shelf during a one-year period, while tidal currents affected about 56.4%. This work was conducted by a team from AGSO, the University of Tasmania, the Australian Bureau of Meteorology, and a Danish geodetic agency.

Geospatial🇦🇺 AustraliaOceanographyMarine PlanningGeological ModellingSediment Mobility+1

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Reinforcement Learning

Impact of runoff on nutrient patterns in northern Port Phillip Bay, Victoria

The Port Phillip Bay Environmental Study measured bay-wide nutrient and chlorophyll a distributions monthly for two years. This dataset focuses on nutrient variation in northern Port Phillip Bay during a high-runoff period in September 1993 and a low-runoff period in January 1995, collected via continuous profiling from a ship. Concentrations of ammonium, oxidised nitrogen, phosphate, and silicate are reported, with highest levels and variability observed on the northern and western perimeters during high runoff.

TabularGeospatialRunoff ImpactPort Phillip BayMarine ChemistryNutrient PollutionCoastal Monitoring+1

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Reinforcement Learning

Financial Allocations Between Linz and Other Authorities, 2019

Cash flows without benefit between the federal, state, EU, other municipalities and the city of Linz for the 2019 fiscal year. The dataset is structured according to the Preliminary and Financial Statements Ordinance (VRV), which defines supporting documents for budget estimates. It was aggregated by Cooperation OGD Österreich and Wikimedia Österreich from the eu_open_data platform.

TabularCSVMunicipal AccountsFinanceCash FlowsLocal GovernmentAustriaPublic Finance+1

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Reinforcement Learning

Clearance of Accounts 2019: Staff and Pension Expenditure for Linz

The dataset contains the 2019 clearance of accounts for the city of Linz, Austria, detailing total staff expenditure and pensions. It is structured according to the Preliminary and Financial Statements Ordinance (VRV), which defines the supporting documents for budget estimates. The data is provided by the Cooperation OGD Österreich and Wikimedia Österreich under a CC-BY-4.0 license.

TabularCSVGovernment ExpenditurePensionsFinanceAustriaStaff CostsPublic Finance+1

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Reinforcement Learning

Bicheno Region Urchin Barrens Scored from AUV Imagery, 2011-2016

Scored time-series data from Autonomous Underwater Vehicle surveys in the Bicheno region of Tasmania between 2011 and 2016. The dataset, associated with a 2020 PLOS ONE article, tracks the presence of Centrostephanus rodgersii urchin barrens inside and outside the Governor Island Marine Reserve. It includes depth and rugosity measures for each image, subsetted to every fifth image to avoid overlap.

TabularTime SeriesGeospatialCSVAuv ImageryComputer VisionMarine BiologyEcological Monitoring+1

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Reinforcement Learning

Analysis of NICS Recruitment Competitions

A report summarising applications and appointments from Northern Ireland Civil Service recruitment competitions across various equality categories. The data covers competitions with a closing date for applications within a calendar year, produced by the Department of Finance and Personnel as Official Statistics. The report includes analysis of various stages between application and appointment.

TabularOfficial StatisticsNorthern IrelandPublic Sector RecruitmentEquality Analysis+1

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Reinforcement Learning

Strategic Opportunity Areas for Great Crested Newt Habitat in North East England

This dataset identifies areas where new ponds would benefit Great Crested Newt populations in Northumberland, Durham, Tyne & Wear, and Tees Valley. It classifies areas as 'Core' (2+ ponds per 25x25m square) or 'Fringe' (1 pond or within 250m of a Core Area) based on predicted newt presence. The data was produced by Natural England in 2021, incorporating land cover, soil, and environmental records from multiple sources.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

Strategic Opportunity Areas for Great Crested Newt Habitat in North and East Yorkshire

Natural England's dataset identifies areas where new ponds would benefit Great Crested Newt populations in North and East Yorkshire. It classifies areas as 'Core' or 'Fringe' based on pond density and predicted newt presence, scoring habitat suitability using features like soil pH, land cover, and distance to water. The data is derived from multiple sources including Ordnance Survey, the Centre for Ecology & Hydrology, and local authorities, with attribution dated 2021.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

GCN - Strategic Opportunity Areas (Leicestershire, Rutland, Rushcliffe and South Kesteven)

Leicestershire, Rutland, Rushcliffe, and South Kesteven areas are mapped to identify locations where new ponds would benefit Great Crested Newt populations. The dataset classifies core and fringe areas based on pond density and predicted newt presence, scoring habitat suitability using features like grassland, woodland, and arable density. It was created by Natural England in 2020, incorporating data from multiple environmental and governmental sources.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

GCN Strategic Opportunity Areas: Great Crested Newt Habitat Suitability in Lancashire

Natural England's 2020 dataset identifies 25x25m grid squares in Lancashire where new ponds would benefit Great Crested Newt populations. It classifies areas as 'Core' or 'Fringe' based on predicted newt presence and pond density, scoring habitat suitability against eight specific environmental features. The data is derived from multiple sources including Ordnance Survey, the Centre for Ecology & Hydrology, and local council records.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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