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.
This scene demonstrates how one might analyze side-by-side the performance of two approaches for agent cover behaviour: scripted vs. Machine Learning (ML).
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.
Demonstrates a code-driven set of general and custom agent behaviors for a unit.
Instance Red/Blue agents through script at runtime.
Examine available terrain maps in TILE or LMAB formats, selecting between different LOD settings.
Red vs Blue networked competitive multiplayer on various terrain maps with dismounted or vehicular units.
Demonstrates terrain map line-of-sight (LOS) system for BLUEFOR, OPFOR and CIV agents.