Learning sensor-agent communication with variable quantizations

In this work the possibility of training a remote (deep) reinforcement learning system was studied. The thesis focuses on the problem of learning to communicate relevant information from a sensor to a reinforcement learning agent. Different quantization strategies were tested in order to balance a trade-off between the effectiveness of the message communicated and the limited communication rate constraint.