Tag: Agents & Entities

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Patrol and Attack Bot

Demonstrates autonomous agents using behavior trees (BT) in order to make decisions about patrolling and attacking.

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Moving Agents

Interface systems for moving agents in scenarios.

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Grouping Agents

Establish groups for agents and relationships between the groups.

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Dynamic Spawn

Spawn individual agents or squads, and then commandeer any single unit with full first-person fire/movement controls.

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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.

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.