How to train a Agent using reinforcement learning for variable step size ?

I have trained a Agent to perform certain task. But it works only for the step size used during training.

My qestion is, how can we train the Agent for variable step size. ?

e.g.

1) train the agent with step size of 1

2) next train the agent with step size of 10

... so on as required.

This could also mean how can we update agent with new learnings ? Without retraining on past dataset

[Emmanouil Tzorakoleftherakis]

1) Can you clarify what you mean by variable step size? Are you referring to the solver options of the Simulink model? Or are you referring to the sample time of the agent?

2) If it's the latter, can you share a scenario/more details on the application? Typically when you train an agent it's for a specific task. Changing the sample time of the agent changes a lot of things. Presumably, similar tasks would have similar bandwidth requirements..