I noticed the RNG behavior in Model, Calling reset_rng() without any arguments doesn’t return the numpy RNG to the state it had when the model was created. Instead, it seems to create a completely new RNG instance seeded from system entropy.

This makes the RNG sequence after a reset different from the sequence right after initialization, even when the model is created with a fixed seed. Since the docstring mentions “if None, reset using the current seed,” this looks unintentional.

The model also stores the initial RNG state in self._rng, but that saved state isn’t used during reset.

• Expected behavior

If a model is initialized with a deterministic seed, calling:

     model.reset_rng()

should bring the numpy RNG back to its initial state so that the sequence of random values matches what we would get from a fresh model run.

• To Reproduce

from mesa import Model

model = Model(seed=42)
initial = model.rng.random()

# Move RNG forward
for _ in range(100):
    model.rng.random()

# Try to reset back to initial state
model.reset_rng()

after_reset = model.rng.random()

print(initial, after_reset)
# These two values do not match

This happens consistently: the RNG after reset does not match the original starting state.

@EwoutH I can work on this! Please let me know.