optax.periodic_update

optax.periodic_update(new_tensors: optax.Params, old_tensors: optax.Params, steps: jax.typing.ArrayLike, update_period: jax.typing.ArrayLike) → optax.Params

Periodically update all parameters with new values.

A slow copy of a model’s parameters, updated every K actual updates, can be used to implement forms of self-supervision (in supervised learning), or to stabilize temporal difference learning updates (in reinforcement learning).

Parameters:

• new_tensors – the latest value of the tensors.

• old_tensors – a slow copy of the model’s parameters.

• steps – number of update steps on the “online” network.

• update_period – every how many steps to update the “target” network.

Returns:

a slow copy of the model’s parameters, updated every update_period steps.

References

[Grill et al., 2020](https://arxiv.org/abs/2006.07733) [Mnih et al., 2015](https://arxiv.org/abs/1312.5602)