> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pioneeroptimisation.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Reward design

> Turning your game's events into a learning signal.

PIO learns from the valued events your game already sends. There is no reward call: your project's reward configuration defines which events matter, how their values combine, and over what window, and the platform derives the reward from that.

## Choosing the signal

Optimise for the outcome you actually want, observed within a window you can wait for. Reward presets cover the common cases, such as first-time-user-experience completion; custom configurations combine your own valued events.

## Pitfalls

* Sparse signals: an event that fires for one player in a thousand gives the model little to learn from. Prefer outcomes most players reach.
* Misaligned values: the model optimises exactly what the values say. If `level_complete` sends 1.0 for a win and 0.0 for a loss, wins are the objective, nothing subtler.
* Dropped values: negative, NaN, and infinite event values never enter the queue, so a signal encoded as negative numbers silently vanishes.


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