  This paper explores the potential of event cameras to enable continuous time
reinforcement learning. We formalise this problem where a continuous stream of
unsynchronised observations is used to produce a corresponding stream of output
actions for the environment. This lack of synchronisation enables greatly
enhanced reactivity. We present a method to train on event streams derived from
standard RL environments, thereby solving the proposed continuous time RL
problem. The CERiL algorithm uses specialised network layers which operate
directly on an event stream, rather than aggregating events into quantised
image frames. We show the advantages of event streams over less-frequent RGB
images. The proposed system outperforms networks typically used in RL, even
succeeding at tasks which cannot be solved traditionally. We also demonstrate
the value of our CERiL approach over a standard SNN baseline using event
streams.
