  The coordination of robotic swarms and the remote wireless control of
industrial systems are among the major use cases for 5G and beyond systems: in
these cases, the massive amounts of sensory information that needs to be shared
over the wireless medium can overload even high-capacity connections.
Consequently, solving the effective communication problem by optimizing the
transmission strategy to discard irrelevant information can provide a
significant advantage, but is often a very complex task. In this work, we
consider a prototypal system in which an observer must communicate its sensory
data to an actor controlling a task (e.g., a mobile robot in a factory). We
then model it as a remote Partially Observable Markov Decision Process (POMDP),
considering the effect of adopting semantic and effective
communication-oriented solutions on the overall system performance. We split
the communication problem by considering an ensemble Vector Quantized
Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement
Learning (DRL) agent to dynamically adapt the quantization level, considering
both the current state of the environment and the memory of past messages. We
tested the proposed approach on the well-known CartPole reference control
problem, obtaining a significant performance increase over traditional
approaches
