I would like some guidance on how to design an Environment for a Reinforcement Learning agent where the stopping conditions and rewards for the environment change based on an initial set of input parameters.
For example, let's say that a system generated alert triggers the instantiation of the RL environment, whereby the RL agent is launched to make decisions in the environment, based on the alert. The alert has two priorities "HIGH" and "LOW", when the priority is "HIGH" the stopping condition is a reward of "100" and when the priority is "LOW", the stopping condition is a reward of "1000".
In this scenario, is it preferable to create two separate environments based on the priority (input parameter) of the alert? Or is this a common requirement that should be designed into the environment/agent? If so, how? Note that I have simplified the scenario, so there could be multiple conditions (e.g., alert, system type, etc), but I am just trying to find a basic solution for the general case.