Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information. One way to tackle this issue is to benefit from the perception of different view points, namely cooperative perception. We propose here a decentralized collaboration, i.e. peer-to-peer, in which the agents are active in their quest for full perception by asking for specific areas in their surroundings on which they would like to know more. Ultimately, we want to optimize a trade-off between the maximization of knowledge about moving objects and the minimization of the total volume of information received from others, to limit communication costs and message processing time. For this, we propose a way to learn a communication policy that reverses the usual communication paradigm by only requesting from other vehicles what is unknown to the ego-vehicle, instead of filtering on the sender side. We tested three different generative models to be taken as base for a Deep Reinforcement Learning (DRL) algorithm, and compared them to a broadcasting policy and a policy randomly selecting areas. In particular, we propose Locally Predictable VAE (LP-VAE), which appears to be producing better belief states for predictions than state-of-the-art models, both as a standalone model and in the context of DRL. Experiments were conducted in the driving simulator CARLA. Our best models reached on average a gain of 25% of the total complementary information, while only requesting about 5% of the ego-vehicle's perceptual field. This trade-off is adjustable through the interpretable hyperparameters of our reward function.