  Humans and animals can learn new skills after practicing for a few hours,
while current reinforcement learning algorithms require a large amount of data
to achieve good performances. Recent model-based approaches show promising
results by reducing the number of necessary interactions with the environment
to learn a desirable policy. However, these methods require biological
implausible ingredients, such as the detailed storage of older experiences, and
long periods of offline learning. The optimal way to learn and exploit
word-models is still an open question. Taking inspiration from biology, we
suggest that dreaming might be an efficient expedient to use an inner model. We
propose a two-module (agent and model) spiking neural network in which
"dreaming" (living new experiences in a model-based simulated environment)
significantly boosts learning. We also explore "planning", an online
alternative to dreaming, that shows comparable performances. Importantly, our
model does not require the detailed storage of experiences, and learns online
the world-model and the policy. Moreover, we stress that our network is
composed of spiking neurons, further increasing the biological plausibility and
implementability in neuromorphic hardware.
