using visual understanding environment layer

#Using visual understanding environment layer download

We provide evidence for our hypothesis by measuring the relationship between interpretability and generalization.įinally, we provide a thorough investigation of several interpretability techniques in the context of RL vision, and pose a number of questions for further research.ĬoinRun is a side-scrolling platformer in which the agent must dodge enemies and other traps and collect the coin at the end of the level. If it is correct, then we can expect RL models to become more interpretable as the environments they are trained on become more diverse. Our results depend on levels in CoinRun being procedurally-generated, leading us to formulate a diversity hypothesis for interpretability. Such editing is only made possible by our previous analysis, and thus provides a quantitative validation of this analysis. We verify the effects of these edits by checking which hazards cause the new agents to fail. We hand-edit the weights of the model to blind the agent to certain hazards, without otherwise changing the agent’s behavior. These were brief enough that they did not affect the agent’s behavior. We find situations when the model “hallucinated” a feature not present in the observation, thereby explaining inaccuracies in the model’s value function. For example, one case of failure was caused by an obstacle being temporarily obscured from view. We perform a step-by-step analysis of the agent’s behavior in cases where it failed to achieve the maximum reward, allowing us to understand what went wrong, and why. We leverage this interface in several ways. Using attribution combined with dimensionality reduction as in, we build an interface for exploring the objects detected by the model, and how they influence its value function and policy.

#Using visual understanding environment layer download

These servers contain service-level metadata, a variety of data visualization previews and download formats, and information for programmers and developers.In this article, we apply interpretability techniques to a reinforcement learning (RL) model trained to play the video game CoinRun. The REST services directory provides access to the GIS servers for NCEI map products. See also: NCEI StoryMap Gallery ArcGIS Service Directories Voices from Florida's Changing Coral Reefs.

Extended Continental Shelf Project: Determining the Extent of U.S. Underwater Frontiers: A Brief History of Seafloor Mapping.Understanding Our Ocean with Water-Column Sonar Data.Tsunamis in History: Using Data to Improve Public Safety.Tracking Tsunamis: Preserving the Historical Record.Listening to the Sea: Using Passive Acoustic Data to Monitor Ships and Ocean Life.In Hot Water: Ocean Heat and Our Warming World.In Harm’s Way: Hurricane Ida’s Impact On Socially Vulnerable Communities.Hydrographic Survey Data / Bathymetric Attributed Grids (BAGs).Earth's Magnetic Field: The Force That's Always With You.They combine maps, narrative text, images, and multimedia with history, to transform data and products into interactive, visual narratives. Observations: Annual / Daily / Hourly and Sub-Hourly / Monthly / AllĮsri StoryMaps present complex scientific or safety information in easy-to-understand terms.Tsunami Events (1850 to Present) Time Lapse Animation.Estimated Tsunami Travel Times to Coastal Locations.Caribbean and Adjacent Regions Tsunami Sources and Models.Natural Hazards, Disasters, and Severe Weather NOAA Ocean Exploration Digital Atlas (old viewer).NOAA Ocean Exploration Data Atlas (new viewer).Ocean Carbon and Acidification Data Portal.Flower Garden Banks National Marine Sanctuary.Harmful Algal Blooms Observing System (HABSOS).Trackline Geophysical Data (new viewer).

Trackline Geophysical Data (old viewer).Index to Marine and Lacustrine Geological Samples.Earth Magnetic Anomaly Grid at 2-arc-minute Resolution, Version 3 (EMAG2v3).Marine Geology, Geophysics, and Bathymetry