  The measurement of time is central to intelligent behavior. We know that both
animals and artificial agents can successfully use temporal dependencies to
select actions. In artificial agents, little work has directly addressed (1)
which architectural components are necessary for successful development of this
ability, (2) how this timing ability comes to be represented in the units and
actions of the agent, and (3) whether the resulting behavior of the system
converges on solutions similar to those of biology. Here we studied interval
timing abilities in deep reinforcement learning agents trained end-to-end on an
interval reproduction paradigm inspired by experimental literature on
mechanisms of timing. We characterize the strategies developed by recurrent and
feedforward agents, which both succeed at temporal reproduction using distinct
mechanisms, some of which bear specific and intriguing similarities to
biological systems. These findings advance our understanding of how agents come
to represent time, and they highlight the value of experimentally inspired
approaches to characterizing agent abilities.
