Is it an open question whether we can do reinforcement learning where the quantum agent is not present in the environment, that is, doesn't contribute noise to the environment? In a classical environment, it seems that the agent is observing the universe while also contributing to it simultaneously. Most papers in this field seem to analyze quantum speed-ups and quantum-inspired algorithms, but I wonder more about classical improvements of the architecture in general.
My aim is to understand if there is a way to arrange an environment where an agent is in a quantum state. The purpose is to separate the agent (actor-critic) from the system. For example, through superposition. In other words, can we do learning with a passive agent?