Category: Example

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Example

Navigation Mesh Generation

Create a navigation mesh from local OWT data for agent pathing.

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Example

Combat

Define the unit spawn parameters of opposing Blue and Red agents. Autonomous behaviors enable each side to navigate obstacles and engage via line-of-sight in order to reach a waypoint.

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Training TakeCover ML

Rapid training of mlAgent units for modelling cover behaviour by inference. This scene links to an external python application over Unity TCP containing user-defined observations created in an academy for which the unit “brains” undergo training.

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Behaviour Comparison

This scene demonstrates how one might analyze side-by-side the performance of two approaches for agent cover behaviour: scripted vs. Machine Learning (ML).

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Agent Commands

Command agents with basic real-time strategy (RTS) control type input. Displays a selector marquee in a specified color, with selected agents displaying a circular icon at their base.

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Agent Behaviours

Demonstrates a code-driven set of general and custom agent behaviors for a unit.

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Adding Agents

Instance Red/Blue agents through script at runtime.

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Terrain Loading

Examine available terrain maps in TILE or LMAB formats, selecting between different LOD settings.

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Team Match

Red vs Blue networked competitive multiplayer on various terrain maps with dismounted or vehicular units.

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Line of Sight

Demonstrates terrain map line-of-sight (LOS) system for BLUEFOR, OPFOR and CIV agents. 

The project/effort/work depicted here was or is sponsored by the U.S. Army Research Laboratory (ARL) under contract number W911NF-14-D-0005. Statements and opinions expressed and content included do not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.