Research guide
Game Telemetry for AI Training
A field-level guide to recording game state, controls, events, versions, and coordinate systems for machine learning.
- For
- Data engineers instrumenting a gameplay collection pipeline
- Reviewed
- July 24, 2026
Direct answer
Useful game telemetry describes what the environment knew at each moment, how that state was measured, and how it aligns with controls and rendered observations.
Record raw and normalized layers
Raw capture preserves source truth. A normalized layer gives researchers a stable interface across sessions or titles. Ship both when possible, with deterministic transformation code and versioned mappings.
Do not flatten semantically different values into one convenient vector without a field dictionary. Position, velocity, camera pose, animation state, and UI state update at different rates and may use different frames of reference.
Name coordinate frames and units
Every pose needs a coordinate frame, handedness, axis convention, unit, and reference object. A vector called position is not portable documentation.
Transforms should declare whether they represent world-to-camera, camera-to-world, local-to-parent, or another relationship. Include calibration and version identifiers when the relationship can change.
Events need lifecycle semantics
An event can be instantaneous, interval-based, predicted, or confirmed later. Define its start, end, source, confidence, and relationship to the episode clock.
Keep presentation events such as replays separate from underlying game events. A goal shown again in a replay should not become a second goal label.
Version the environment
Game patch, map, server configuration, mod list, graphics settings, control mapping, and capture build all affect reproducibility. Store them as structured fields rather than prose in a release note.
When an update changes physics or telemetry semantics, create a new dataset version and document compatibility. Silent mixing turns model errors into archaeology.