How to encode variable-length matrix into a single vector for agent observations

I’m writing a reinforcement learning agent that has to navigate through a series of rooms in order to find the room it’s looking for. As it navigates through rooms, those rooms make up the observation. Each room is represented by a 384-dimensional vector. So the number of vectors changes over time. But the number of discovered rooms can be incredibly large, up to 1000. How can I train an encoding model to condense these 384-dimensional vectors down into a single vector representation to use as the observation for my agent?