  Low precision networks in the reinforcement learning (RL) setting are
relatively unexplored because of the limitations of binary activations for
function approximation. Here, in the discrete action ATARI domain, we
demonstrate, for the first time, that low precision policy distillation from a
high precision network provides a principled, practical way to train an RL
agent. As an application, on 10 different ATARI games, we demonstrate real-time
end-to-end game playing on low-power neuromorphic hardware by converting a
sequence of game frames into discrete actions.
