Examples

Instance Red/Blue agents through script at runtime.

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

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 how to setup a scene to use VHMsg (ActiveMQ wrapper) in RIDE.

This example demonstrates how to load and play audio clips in RIDE.

Demonstrate various ICT S&T research lines in a well-known format with very specific pass/fail parameters and training objectives.

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

Demonstrates how to use RIDE’s tween (in-betweening) system to move a camera.

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.

Collects the available UI elements (widgets) in RIDE.

Demonstrates how to use RIDE systems to send and receive Distributed Interactive Simulation (DIS) messages.

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

Shows how to upload and download files from cloud services AWS S3 and Azure Blob.

Establish groups for agents and relationships between the groups.

If the CommandLineParser.cs script is in the scene, and you create a build of the scene, then you can process all the ScenarioParameters and run your simulation with or without graphics.

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

Create listeners for events and dispatch events in simulations.

Use the RIDE API to implement your own logging.

Interface systems for moving agents in scenarios.

Master scenario events list injection (MSELI) allows you to create arbitrary events in the simulation and provide them with execution orders and conditions for their execution during the scenario.

Create a navigation mesh from OWT data for agent pathing.

Displays how to create particle systems from prefabs, scene objects, resources, and RideIDs.

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

Logs pedagogical events during an active simulation for later analysis.

This example scene shows how to create an interactive tour around a terrain using scenario events and custom AI behaviors.

Demonstrates how to host a RESTful service in RIDE so that it can be accessed via an HTTP request from a web browser.

Demonstrates how to spawn and move opposing agents.

Provides the ability to record a session and then play back that session recording.

Demonstrates how to open, send data over, and close a TCP socket in RIDE.

Load OWT Terrain from disk, create agents from prefabs or from the scene.

Demonstrates how to use Microsoft speech recognition in RIDE.

Basic Agent state machine that can be modified as needed for simulations.

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

Apply explosive ordnance of user-set intensity and size at any location on a terrain map. Deformation of the surface geometry may change depending on the material/structure classification at that point.

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

Demonstrates how to control the speed of the simulation.

Collects the available SpeedTree assets that will be used to make asset bundles for different platforms.

Save agents and/or groups for later retrieval, allowing them to be loaded into the scene via custom menus.

Navigate terrain maps with “drivable” vehicles through basic keyboard or gamepad input. Cycle multiple camera angles in and around the vehicle for observation of variables in the environment that affect physics of the vehicles. An overlay displays vehicle motion statistics in real-time.

Configure a scalability test with generic in-game agents in real-time and record performance data locally and in the cloud for later analysis.