Note: The RNG does not have an avalanche effect, and can output similar random streams given similar seeds. Consider using a hash function to improve your seed quality if they're sourced externally.
Note: Setting this property produces a side effect of changing the internal state, so make sure to initialize the seed before modifying the state:
Note: The default value of this property is pseudo-random, and changes when calling randomize. The 0 value documented here is a placeholder, and not the actual default seed.
var rng = RandomNumberGenerator.new() rng.seed = hash("Godot") rng.state = 100 # Restore to some previously saved state.

var rng = RandomNumberGenerator.new() print(rng.randf()) var saved_state = rng.state # Store current state. print(rng.randf()) # Advance internal state. rng.state = saved_state # Restore the state. print(rng.randf()) # Prints the same value as previously.
Note: Do not set state to arbitrary values, since the random number generator requires the state to have certain qualities to behave properly. It should only be set to values that came from the state property itself. To initialize the random number generator with arbitrary input, use seed instead.
Note: The default value of this property is pseudo-random, and changes when calling randomize. The 0 value documented here is a placeholder, and not the actual default state.

var rng = RandomNumberGenerator.new() var my_array = ["one", "two", "three", "four"] var weights = PackedFloat32Array([0.5, 1, 1, 2]) # Prints one of the four elements in `my_array`. # It is more likely to print "four", and less likely to print "one". print(my_array[rng.rand_weighted(weights)])

Note: This method uses the [url=https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform]Box-Muller transform[/url] algorithm.