  Achieving energy efficiency in learning is a key challenge for artificial
intelligence (AI) computing platforms. Biological systems demonstrate
remarkable abilities to learn complex skills quickly and efficiently. Inspired
by this, we present a hardware implementation of model-based reinforcement
learning (MBRL) using spiking neural networks (SNNs) on mixed-signal
analog/digital neuromorphic hardware. This approach leverages the energy
efficiency of mixed-signal neuromorphic chips while achieving high sample
efficiency through an alternation of online learning, referred to as the
"awake" phase, and offline learning, known as the "dreaming" phase. The model
proposed includes two symbiotic networks: an agent network that learns by
combining real and simulated experiences, and a learned world model network
that generates the simulated experiences. We validate the model by training the
hardware implementation to play the Atari game Pong. We start from a baseline
consisting of an agent network learning without a world model and dreaming,
which successfully learns to play the game. By incorporating dreaming, the
number of required real game experiences are reduced significantly compared to
the baseline. The networks are implemented using a mixed-signal neuromorphic
processor, with the readout layers trained using a computer in-the-loop, while
the other layers remain fixed. These results pave the way toward
energy-efficient neuromorphic learning systems capable of rapid learning in
real world applications and use-cases.
